From 812b532a671a1cbf94098347d42aef765131eab0 Mon Sep 17 00:00:00 2001
From: Alex Do <alexxdo2004@gmail.com>
Date: Sun, 7 Jun 2026 03:09:29 +0300
Subject: [PATCH 1/5] Remove stale top-level scenario_generation duplicate; add
 qGAN training driver

The "move scenario_generation into src/" commit left a byte-identical (and in
places stale) top-level copy. src/scenario_generation is canonical: pytest's
pythonpath=["src"] resolves there, and its io.py/inspect_generated.py are the
refactored versions. Delete the top-level duplicate.

Add scripts/train_qgan.py: trains the experimental adversarial Born-machine
generator on a synthetic n=12 spec and validates generated marginals/correlations
against the Gaussian-copula target. (The canonical QCBM loader uses analytic
angles and needs no training; this exercises the qGAN path.)

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 scenario_generation/__init__.py          |   5 -
 scenario_generation/cli.py               | 140 -------------------
 scenario_generation/gam_generator.py     |  84 -----------
 scenario_generation/inspect_generated.py |  56 --------
 scenario_generation/io.py                |  27 ----
 scenario_generation/plots.py             |  37 -----
 scenario_generation/qcbm_trainer.py      |  86 ------------
 scenario_generation/qgan_trainer.py      | 170 -----------------------
 scenario_generation/quantum_hybrid.py    |  70 ----------
 scenario_generation/validation.py        |  60 --------
 scripts/train_qgan.py                    | 107 ++++++++++++++
 11 files changed, 107 insertions(+), 735 deletions(-)
 delete mode 100644 scenario_generation/__init__.py
 delete mode 100644 scenario_generation/cli.py
 delete mode 100644 scenario_generation/gam_generator.py
 delete mode 100644 scenario_generation/inspect_generated.py
 delete mode 100644 scenario_generation/io.py
 delete mode 100644 scenario_generation/plots.py
 delete mode 100644 scenario_generation/qcbm_trainer.py
 delete mode 100644 scenario_generation/qgan_trainer.py
 delete mode 100644 scenario_generation/quantum_hybrid.py
 delete mode 100644 scenario_generation/validation.py
 create mode 100644 scripts/train_qgan.py

diff --git a/scenario_generation/__init__.py b/scenario_generation/__init__.py
deleted file mode 100644
index 97170fa..0000000
--- a/scenario_generation/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .io import load_system_spec, save_scenarios
-from .gam_generator import GAMGenerator
-from .validation import validate_scenarios
-
-__all__ = ["load_system_spec", "save_scenarios", "GAMGenerator", "validate_scenarios"]
diff --git a/scenario_generation/cli.py b/scenario_generation/cli.py
deleted file mode 100644
index c67f2c1..0000000
--- a/scenario_generation/cli.py
+++ /dev/null
@@ -1,140 +0,0 @@
-from __future__ import annotations
-
-import argparse
-import sys
-import numpy as np
-
-from scenario_generation.io import load_system_spec, save_scenarios
-from scenario_generation.gam_generator import GAMGenerator
-
-
-def main(argv: list[str] | None = None) -> int:
-    parser = argparse.ArgumentParser(description="Generate synthetic scenarios with GAM")
-    parser.add_argument("--system-spec", required=True)
-    parser.add_argument("--n-scenarios", type=int, default=1000)
-    parser.add_argument("--out", required=True)
-    parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument("--augmentation-strength", type=float, default=0.10,
-                        help="probability that an exposure-triggered buffer breach becomes a default (0..1)")
-    parser.add_argument("--corr-threshold", type=float, default=0.10,
-                        help="maximum allowed mean absolute off-diagonal correlation drift before warning/failing")
-    parser.add_argument("--fail-on-drift", action="store_true",
-                        help="exit nonzero if correlation drift exceeds --corr-threshold")
-    parser.add_argument("--hybrid", action="store_true",
-                        help="mix a fraction of quantum samples into the generated scenarios")
-    parser.add_argument("--quantum-fraction", type=float, default=0.05,
-                        help="fraction of samples drawn from the entangled quantum generator (0..1)")
-    parser.add_argument("--quantum-ansatz", type=str, default="entangled",
-                        help="ansatz passed to EntangledBornMachineGenerator (entangled|ghz_systemic)")
-    parser.add_argument("--quantum-calibrate", action="store_true",
-                        help="ask the entangled generator to calibrate against exact statevector moments when fitting")
-    parser.add_argument("--quantum-calibration-iterations", type=int, default=30,
-                        help="number of calibration iterations for the entangled generator")
-    parser.add_argument("--train-qcbm", action="store_true",
-                        help="train a QCBM (light) against the system spec and produce comparison plots")
-    parser.add_argument("--qcbm-maxiter", type=int, default=20,
-                        help="max optimizer iterations per block for the QCBM trainer (keeps training light)")
-    parser.add_argument("--report-n-samples", type=int, dest="report_n_samples", default=2000,
-                        help="how many samples to draw for the comparison report")
-    parser.add_argument("--report-out", type=str, default="outputs/reports",
-                        help="directory to write report plots")
-    args = parser.parse_args(argv)
-
-    spec = load_system_spec(args.system_spec)
-    if args.hybrid:
-        from scenario_generation.quantum_hybrid import HybridGAMGenerator
-
-        ent_args = {
-            "ansatz": args.quantum_ansatz,
-            "calibrate": args.quantum_calibrate,
-            "calibration_iterations": args.quantum_calibration_iterations,
-        }
-        gen = HybridGAMGenerator(quantum_fraction=args.quantum_fraction, entangled_kwargs=ent_args, augmentation_strength=args.augmentation_strength)
-        gen.fit(spec)
-        samples = gen.sample(args.n_scenarios, seed=args.seed)
-    else:
-        gen = GAMGenerator(augmentation_strength=args.augmentation_strength)
-        gen.fit(spec)
-        samples = gen.sample(args.n_scenarios, seed=args.seed)
-    save_scenarios(args.out, samples, spec.node_names)
-    print(f"Wrote {samples.shape[0]} scenarios to {args.out}")
-
-    # Validate correlation drift using validation helper
-    from scenario_generation.validation import validate_pairwise_corr
-
-    sampled_corr, target_corr = validate_pairwise_corr(samples, spec)
-    off = np.triu_indices(sampled_corr.shape[0], k=1)
-    mean_abs_drift = float(np.mean(np.abs(sampled_corr[off] - target_corr[off])))
-    print(f"mean_abs_correlation_drift={mean_abs_drift:.6f}")
-    if mean_abs_drift > args.corr_threshold:
-        msg = f"Correlation drift {mean_abs_drift:.6f} exceeds threshold {args.corr_threshold:.6f}"
-        if args.fail_on_drift:
-            print("ERROR:", msg)
-            return 2
-        else:
-            print("WARNING:", msg)
-    # optional training & comparison report
-    if args.train_qcbm:
-        from scenario_generation.qcbm_trainer import QCBMTrainer
-        from scenario_generation.plots import plot_marginals, plot_corrs
-        from systemic_risk.generators.quantum import ansatz as A
-
-        trainer = QCBMTrainer(maxiter=args.qcbm_maxiter)
-        print("Training QCBM (light) ...")
-        results = trainer.fit(spec)
-        # build statevector samples from trained circuits (small blocks only)
-        # For simplicity, sample from each trained block exact probabilities and combine
-        built_probs = []
-        for block_key, info in results.items():
-            circ = info['circuit']
-            # exact statevector for block
-            from systemic_risk.generators.quantum.statevector import StateVector
-
-            sv = StateVector(circ.size)
-            for i, th in enumerate(circ.ry):
-                sv.ry(i, th)
-            for e, (cidx, tidx) in enumerate(circ.edges):
-                sv.cry(cidx, tidx, circ.cry[e])
-            built_probs.append((circ.qubits, sv.probabilities()))
-        # For reporting, compare marginals/corrs on full spec via entangled generator seed
-        # assemble a full sampled distribution by drawing 2000 samples from hybrid gen
-        import numpy as _np
-
-        sample_for_report = samples[: args.report_n_samples]
-        sampled_marg = sample_for_report.mean(axis=0)
-        # compute sampled corr
-        pairwise_joint = (sample_for_report.T @ sample_for_report) / max(sample_for_report.shape[0], 1)
-        sampled_corr = _np.eye(spec.n)
-        for i in range(spec.n):
-            for j in range(i + 1, spec.n):
-                denom = _np.sqrt(sampled_marg[i] * (1 - sampled_marg[i]) * sampled_marg[j] * (1 - sampled_marg[j]))
-                corr = 0.0 if denom == 0 else (pairwise_joint[i, j] - sampled_marg[i] * sampled_marg[j]) / denom
-                sampled_corr[i, j] = sampled_corr[j, i] = float(_np.clip(corr, -1.0, 1.0))
-
-        # target corr
-        from systemic_risk.spec import joint_to_corr
-
-        target_corr = joint_to_corr(spec.target_pairwise_joint_probs(), spec.marginal_default_probs)
-        # save plots
-        plot_marginals(spec.marginal_default_probs, sampled_marg, args.report_out / 'marginals.png')
-        plot_corrs(target_corr, sampled_corr, args.report_out / 'corrs.png')
-        print(f"Saved report plots to {args.report_out}")
-    # print diagnostics if available
-    if hasattr(gen, "_last_diagnostics"):
-        pre = gen._last_diagnostics.get("pre")
-        post = gen._last_diagnostics.get("post")
-        if pre is not None and post is not None:
-            print(f"pre_marginal_mean={float(pre.sampled_marginals.mean()):.6f}")
-            print(f"post_marginal_mean={float(post.sampled_marginals.mean()):.6f}")
-            # show small summary of pairwise corr change
-            if pre.sampled_pairwise_corr is not None and post.sampled_pairwise_corr is not None:
-                pre_corr = pre.sampled_pairwise_corr
-                post_corr = post.sampled_pairwise_corr
-                off = np.triu_indices(pre_corr.shape[0], k=1)
-                print(f"pre_post_corr_mean_abs_diff={float(abs(post_corr[off]-pre_corr[off]).mean()):.6f}")
-    return 0
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/scenario_generation/gam_generator.py b/scenario_generation/gam_generator.py
deleted file mode 100644
index 9c1a990..0000000
--- a/scenario_generation/gam_generator.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-from typing import Optional
-
-from systemic_risk.spec import SystemSpec
-from systemic_risk.generators.base import ScenarioGenerator
-from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator
-
-
-class GAMGenerator(ScenarioGenerator):
-    """Generative Augmentation Model (GAM).
-
-    Strategy (simple, reproducible):
-    - Use the Gaussian copula baseline to match target marginals and latent correlations
-    - Apply a single-step exposure-aware augmentation: if the initial sampled defaults
-      imply losses to an institution that exceed its buffer, mark it defaulted.
-
-    This preserves schema and approximately preserves marginal / pairwise structure
-    while injecting exposure-aware stress behaviour.
-    """
-
-    name = "GAM generator"
-
-    def __init__(self, augmentation_strength: float = 0.10) -> None:
-        """augmentation_strength: probability in [0,1] that an exposure-triggered
-        buffer-breach converts into an actual default in the augmented sample.
-
-        Setting this low preserves the Gaussian-copula dependency structure while
-        allowing exposure-aware stress to appear occasionally. Default=0.10.
-        """
-        if not (0.0 <= augmentation_strength <= 1.0):
-            raise ValueError("augmentation_strength must lie in [0, 1]")
-        self.augmentation_strength = float(augmentation_strength)
-        self._copula: Optional[GaussianCopulaGenerator] = None
-        self._spec: Optional[SystemSpec] = None
-
-    def fit(self, spec: SystemSpec) -> None:
-        self._spec = spec
-        gc = GaussianCopulaGenerator()
-        gc.fit(spec)
-        self._copula = gc
-
-    def sample(self, n_samples: int, seed: int | None = None) -> np.ndarray:
-        if self._copula is None or self._spec is None:
-            raise RuntimeError("GAMGenerator must be fit before sampling")
-        rng = np.random.default_rng(seed)
-        base = self._copula.sample(n_samples, seed=seed)
-
-        # compute diagnostics before augmentation
-        try:
-            pre_diag = self._copula.diagnostics(base)
-        except Exception:
-            pre_diag = None
-
-        # exposure-aware probabilistic augmentation (reduces correlation drift)
-        W = np.asarray(self._spec.exposure_matrix, dtype=float)
-        buffers = np.asarray(self._spec.capital_buffers, dtype=float)
-        # losses to each institution i for each sample: losses[s,i] = sum_j base[s,j]*W[i,j]
-        losses = base @ W.T
-        trigger = losses > buffers
-        if self.augmentation_strength <= 0.0:
-            augmented_mask = np.zeros_like(trigger, dtype=bool)
-        elif self.augmentation_strength >= 1.0:
-            augmented_mask = trigger.copy()
-        else:
-            coin = rng.random(size=trigger.shape)
-            augmented_mask = trigger & (coin < self.augmentation_strength)
-
-        final = np.clip(base | augmented_mask.astype(int), 0, 1).astype(int)
-
-        # compute diagnostics after augmentation
-        try:
-            post_diag = self.diagnostics(final)
-        except Exception:
-            post_diag = None
-
-        # attach diagnostics to object for inspection (not part of ScenarioGenerator API)
-        self._last_diagnostics = {
-            "pre": pre_diag,
-            "post": post_diag,
-            "augmentation_strength": self.augmentation_strength,
-        }
-        return final
diff --git a/scenario_generation/inspect_generated.py b/scenario_generation/inspect_generated.py
deleted file mode 100644
index ec44320..0000000
--- a/scenario_generation/inspect_generated.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from __future__ import annotations
-
-import csv
-import numpy as np
-from systemic_risk.data.loaders import load_system_spec
-from systemic_risk.simulator.cascade import simulate_many
-from systemic_risk.spec import joint_to_corr
-
-
-def main():
-    spec = load_system_spec('outputs/data_network/system_spec.json')
-    path = 'outputs/scenarios/gam_scenarios.csv'
-    with open(path, 'r', encoding='utf-8') as f:
-        reader = csv.reader(f)
-        header = next(reader)
-        data = np.array([list(map(int, row)) for row in reader], dtype=int)
-
-    n_samples, n = data.shape
-    print('csv_path:', path)
-    print('shape:', data.shape)
-    print('header_count:', len(header), 'spec.n:', spec.n)
-
-    sampled_marginals = data.mean(axis=0)
-    target_marginals = np.asarray(spec.marginal_default_probs)
-    max_abs_diff = float(np.max(np.abs(sampled_marginals - target_marginals)))
-    mean_abs_diff = float(np.mean(np.abs(sampled_marginals - target_marginals)))
-    print(f'marginals: target mean={float(target_marginals.mean()):.6f}, sampled mean={float(sampled_marginals.mean()):.6f}')
-    print(f'marginals: max_abs_diff={max_abs_diff:.6f}, mean_abs_diff={mean_abs_diff:.6f}')
-
-    # sample correlation
-    m = sampled_marginals
-    pairwise_joint = (data.T @ data) / max(n_samples, 1)
-    sampled_corr = np.eye(n)
-    for i in range(n):
-        for j in range(i + 1, n):
-            denom = np.sqrt(m[i] * (1 - m[i]) * m[j] * (1 - m[j]))
-            corr = 0.0 if denom == 0 else (pairwise_joint[i, j] - m[i] * m[j]) / denom
-            sampled_corr[i, j] = sampled_corr[j, i] = float(np.clip(corr, -1.0, 1.0))
-
-    if spec.target_pairwise_corr is not None:
-        target_corr = np.asarray(spec.target_pairwise_corr)
-    else:
-        target_corr = joint_to_corr(spec.target_pairwise_joint_probs(), np.asarray(spec.marginal_default_probs))
-
-    off = np.triu_indices(n, k=1)
-    abs_diff_corr = np.abs(sampled_corr[off] - target_corr[off])
-    print(f'corr_offdiag: mean_abs_diff={float(abs_diff_corr.mean()):.6f}, max_abs_diff={float(abs_diff_corr.max()):.6f}')
-
-    # run simulator on first 100 scenarios to confirm acceptance
-    sample_subset = data[:100]
-    results = simulate_many(sample_subset, spec)
-    print('simulate_many accepted sample subset:', len(results), 'examples: first failure_count=', results[0].failure_count)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/scenario_generation/io.py b/scenario_generation/io.py
deleted file mode 100644
index abe0583..0000000
--- a/scenario_generation/io.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-import csv
-import numpy as np
-
-from systemic_risk.spec import SystemSpec
-from systemic_risk.data.loaders import load_system_spec as _load_spec
-
-
-def load_system_spec(path: str | Path) -> SystemSpec:
-    return _load_spec(path)
-
-
-def save_scenarios(path: str | Path, samples: np.ndarray, node_names: list[str]) -> None:
-    path = Path(path)
-    path.parent.mkdir(parents=True, exist_ok=True)
-    samples = np.asarray(samples, dtype=int)
-    n_samples, n = samples.shape
-    if len(node_names) != n:
-        raise ValueError("node_names length must match number of columns in samples")
-    # Save as CSV with header of node names, one scenario per row
-    with path.open("w", newline="", encoding="utf-8") as f:
-        writer = csv.writer(f)
-        writer.writerow(node_names)
-        for row in samples:
-            writer.writerow(row.tolist())
diff --git a/scenario_generation/plots.py b/scenario_generation/plots.py
deleted file mode 100644
index 600aaab..0000000
--- a/scenario_generation/plots.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-import matplotlib.pyplot as plt
-from pathlib import Path
-
-
-def plot_marginals(target: np.ndarray, sampled: np.ndarray, out: str | Path) -> None:
-    out = Path(out)
-    out.parent.mkdir(parents=True, exist_ok=True)
-    idx = np.arange(len(target))
-    plt.figure(figsize=(8, 4))
-    plt.bar(idx - 0.2, target, width=0.4, label='target')
-    plt.bar(idx + 0.2, sampled, width=0.4, label='sampled')
-    plt.xlabel('node')
-    plt.ylabel('marginal P(default)')
-    plt.legend()
-    plt.tight_layout()
-    plt.savefig(str(out))
-    plt.close()
-
-
-def plot_corrs(target: np.ndarray, sampled: np.ndarray, out: str | Path) -> None:
-    out = Path(out)
-    out.parent.mkdir(parents=True, exist_ok=True)
-    plt.figure(figsize=(10, 4))
-    plt.subplot(1, 2, 1)
-    plt.imshow(target, cmap='coolwarm', vmin=-1, vmax=1)
-    plt.title('target correlation')
-    plt.colorbar(fraction=0.046, pad=0.04)
-    plt.subplot(1, 2, 2)
-    plt.imshow(sampled, cmap='coolwarm', vmin=-1, vmax=1)
-    plt.title('sampled correlation')
-    plt.colorbar(fraction=0.046, pad=0.04)
-    plt.tight_layout()
-    plt.savefig(str(out))
-    plt.close()
diff --git a/scenario_generation/qcbm_trainer.py b/scenario_generation/qcbm_trainer.py
deleted file mode 100644
index 570a63a..0000000
--- a/scenario_generation/qcbm_trainer.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-from typing import Callable
-
-from scipy.optimize import minimize
-
-from systemic_risk.spec import SystemSpec
-from systemic_risk.generators.quantum import ansatz as A
-from systemic_risk.generators.quantum.statevector import StateVector
-
-
-def _block_loss(params: np.ndarray, circuit: A.EntangledCircuit) -> float:
-    # unpack params into ry and cry
-    n_ry = circuit.size
-    n_cry = len(circuit.edges)
-    ry = params[:n_ry]
-    cry = params[n_ry : n_ry + n_cry]
-
-    # build statevector for block
-    sv = StateVector(circuit.size)
-    for i, theta in enumerate(ry):
-        sv.ry(i, theta)
-    for e, (control, target) in enumerate(circuit.edges):
-        sv.cry(control, target, cry[e])
-
-    marg = sv.marginals()
-    joint = sv.pairwise_joint()
-    # target moments
-    tp = circuit.target_p
-    tj = circuit.target_cov + np.outer(tp, tp)
-
-    # loss: weighted sum of marginal and joint squared errors
-    lm = float(np.sum((marg - tp) ** 2))
-    lj = float(np.sum((joint - tj) ** 2))
-    return lm + lj
-
-
-class QCBMTrainer:
-    """Simple trainer that refines analytic angles per block via L-BFGS-B.
-
-    This is a light-weight optimisation harness (not a full qGAN). It accepts
-    analytic seed circuits from :mod:`systemic_risk.generators.quantum.ansatz`
-    and refines ``RY`` and ``CRY`` angles jointly to reduce marginal/joint
-    moment error under the exact statevector simulator.
-    """
-
-    def __init__(self, maxiter: int = 50):
-        self.maxiter = int(maxiter)
-
-    def fit(self, spec: SystemSpec, *, edges: list[tuple[int, int]] | None = None) -> dict:
-        if edges is None:
-            edges = A.dependency_edges(spec, threshold=0.02, within_clusters_only=False)
-        cov = A.target_covariance(spec)
-        p = np.clip(spec.marginal_default_probs, 1e-6, 1.0 - 1e-6)
-
-        # partition into blocks
-        blocks = A.partition_blocks(spec, edges, max_block=spec.n)
-        results = {}
-        for qubits in blocks:
-            circuit = A._block_circuit(qubits, p, cov, edges)
-            # initial calibration (light)
-            circuit = A.calibrate_block(circuit, lambda ry, edges, cry: self._moments_fn(ry, edges, cry), iterations=5)
-
-            # pack params
-            x0 = np.concatenate([circuit.ry, circuit.cry])
-            bounds = [(1e-6, np.pi - 1e-6)] * circuit.size + [(-2.5, 2.5)] * len(circuit.edges)
-
-            res = minimize(lambda x: _block_loss(x, circuit), x0, method="L-BFGS-B", bounds=bounds, options={"maxiter": self.maxiter})
-            x_opt = res.x
-            ry_opt = x_opt[: circuit.size]
-            cry_opt = x_opt[circuit.size : circuit.size + len(circuit.edges)]
-
-            circuit_opt = A.EntangledCircuit(qubits=circuit.qubits, ry=ry_opt, edges=circuit.edges, cry=cry_opt, target_p=circuit.target_p, target_cov=circuit.target_cov)
-            results[tuple(qubits)] = {"circuit": circuit_opt, "opt_result": res}
-        return results
-
-    @staticmethod
-    def _moments_fn(ry: np.ndarray, edges: list[tuple[int, int]], cry: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-        n = ry.size
-        sv = StateVector(n)
-        for i, theta in enumerate(ry):
-            sv.ry(i, theta)
-        for e, (control, target) in enumerate(edges):
-            sv.cry(control, target, cry[e])
-        return sv.marginals(), sv.pairwise_joint()
diff --git a/scenario_generation/qgan_trainer.py b/scenario_generation/qgan_trainer.py
deleted file mode 100644
index 1fa33c4..0000000
--- a/scenario_generation/qgan_trainer.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-from typing import Tuple
-
-from systemic_risk.generators.quantum import ansatz as A
-from systemic_risk.generators.quantum.statevector import StateVector, sample_bitstrings
-from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator
-
-
-class SimpleDiscriminator:
-    """Small feedforward discriminator implemented in NumPy.
-
-    Binary classifier returning probability P(real). Trained with SGD on
-    cross-entropy.
-    """
-
-    def __init__(self, input_dim: int, hidden: int = 32, seed: int | None = None):
-        rng = np.random.default_rng(seed)
-        self.W1 = rng.normal(scale=0.1, size=(input_dim, hidden))
-        self.b1 = np.zeros(hidden)
-        self.W2 = rng.normal(scale=0.1, size=(hidden, 1))
-        self.b2 = np.zeros(1)
-
-    def forward(self, X: np.ndarray) -> np.ndarray:
-        h = np.tanh(X @ self.W1 + self.b1)
-        logits = h @ self.W2 + self.b2
-        probs = 1.0 / (1.0 + np.exp(-logits))
-        return probs.ravel()
-
-    def train_step(self, X_real: np.ndarray, X_fake: np.ndarray, lr: float = 1e-2) -> float:
-        X = np.vstack([X_real, X_fake])
-        y = np.concatenate([np.ones(X_real.shape[0]), np.zeros(X_fake.shape[0])])
-        probs = self.forward(X)
-        # cross-entropy loss
-        eps = 1e-12
-        loss = -np.mean(y * np.log(probs + eps) + (1 - y) * np.log(1 - probs + eps))
-
-        # gradients via simple backprop
-        h = np.tanh(X @ self.W1 + self.b1)
-        dlogits = (probs - y)[:, None] / X.shape[0]
-        dW2 = h.T @ dlogits
-        db2 = dlogits.sum(axis=0)
-        dh = dlogits @ self.W2.T
-        drelu = (1.0 - h ** 2) * dh
-        dW1 = X.T @ drelu
-        db1 = drelu.sum(axis=0)
-
-        # SGD step
-        self.W2 -= lr * dW2
-        self.b2 -= lr * db2
-        self.W1 -= lr * dW1
-        self.b1 -= lr * db1
-        return float(loss)
-
-
-class QGANTrainer:
-    """Adversarial trainer for a parameterised Born machine using a NumPy discriminator.
-
-    Notes:
-    - This is a simple, small-scale qGAN suitable for statevector-backed training.
-    - Generator parameters are the block `RY` and `CRY` angles from `ansatz._block_circuit`.
-    - Generator gradients are estimated by forward finite differences on the exact
-      statevector-based expectation of the discriminator loss.
-    """
-
-    def __init__(
-        self,
-        n_epochs: int = 10,
-        batch_size: int = 256,
-        disc_steps: int = 5,
-        gen_steps: int = 1,
-        lr_disc: float = 1e-2,
-        lr_gen: float = 1e-2,
-        fd_eps: float = 1e-3,
-        seed: int | None = None,
-    ) -> None:
-        self.n_epochs = int(n_epochs)
-        self.batch_size = int(batch_size)
-        self.disc_steps = int(disc_steps)
-        self.gen_steps = int(gen_steps)
-        self.lr_disc = float(lr_disc)
-        self.lr_gen = float(lr_gen)
-        self.fd_eps = float(fd_eps)
-        self.rng = np.random.default_rng(seed)
-
-    def _build_circuit_params(self, circuit: A.EntangledCircuit) -> np.ndarray:
-        return np.concatenate([circuit.ry, circuit.cry])
-
-    def _unpack_params(self, circuit: A.EntangledCircuit, params: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-        n = circuit.size
-        ry = params[:n]
-        cry = params[n : n + len(circuit.edges)]
-        return ry, cry
-
-    def _sample_from_params(self, circuit: A.EntangledCircuit, params: np.ndarray, n_samples: int) -> np.ndarray:
-        ry, cry = self._unpack_params(circuit, params)
-        sv = StateVector(circuit.size)
-        for i, th in enumerate(ry):
-            sv.ry(i, float(th))
-        for e, (cidx, tidx) in enumerate(circuit.edges):
-            sv.cry(int(cidx), int(tidx), float(cry[e]))
-        probs = sv.probabilities()
-        bits = sample_bitstrings(probs, circuit.size, n_samples, self.rng)
-        # map to global indices: produce full-length vector with zeros then fill qubits
-        out = np.zeros((n_samples, max(circuit.qubits) + 1), dtype=int)
-        for col, q in enumerate(circuit.qubits):
-            out[:, q] = bits[:, col]
-        return out
-
-    def fit(self, spec, *, n_real: int = 2000, block_qubits: list[int] | None = None):
-        # construct real samples via Gaussian copula baseline (moment-matched)
-        gauss = GaussianCopulaGenerator()
-        gauss.fit(spec)
-        real = gauss.sample(n_real, seed=self.rng.integers(1 << 30))
-
-        # choose one analytic block circuit to train (small systems / demo)
-        edges = A.dependency_edges(spec, threshold=0.02, within_clusters_only=False)
-        blocks = A.partition_blocks(spec, edges, max_block=spec.n)
-        # pick smallest block with size <= 12 for tractability
-        chosen = None
-        for b in blocks:
-            if len(b) <= 12:
-                chosen = b
-                break
-        if chosen is None:
-            chosen = blocks[0]
-        circuit = A._block_circuit(chosen, np.clip(spec.marginal_default_probs, 1e-6, 1 - 1e-6), A.target_covariance(spec), edges)
-
-        # initial params
-        theta = self._build_circuit_params(circuit)
-        disc = SimpleDiscriminator(input_dim=spec.n, hidden=32, seed=int(self.rng.integers(1 << 30)))
-
-        history = {"disc_loss": [], "gen_loss": []}
-        for epoch in range(self.n_epochs):
-            # discriminator steps
-            for _ in range(self.disc_steps):
-                idx = self.rng.integers(0, real.shape[0], size=self.batch_size)
-                Xr = real[idx]
-                Xf = self._sample_from_params(circuit, theta, self.batch_size)
-                dl = disc.train_step(Xr, Xf, lr=self.lr_disc)
-            history["disc_loss"].append(dl)
-
-            # generator steps (finite-difference gradient estimate)
-            for _ in range(self.gen_steps):
-                # approximate loss = mean log(1 - D(x)) over samples
-                Xf = self._sample_from_params(circuit, theta, self.batch_size)
-                probs = disc.forward(Xf)
-                eps = 1e-12
-                gen_loss = float(np.mean(np.log(1.0 - probs + eps)))
-                history["gen_loss"].append(gen_loss)
-
-                # finite-difference gradient
-                grad = np.zeros_like(theta)
-                base_loss = gen_loss
-                for i in range(theta.size):
-                    theta_p = theta.copy()
-                    theta_p[i] += self.fd_eps
-                    Xp = self._sample_from_params(circuit, theta_p, self.batch_size)
-                    pp = disc.forward(Xp)
-                    loss_p = float(np.mean(np.log(1.0 - pp + eps)))
-                    grad[i] = (loss_p - base_loss) / self.fd_eps
-
-                # gradient ascent (we maximize log(1-D) to fool discriminator); use lr_gen
-                theta = theta + self.lr_gen * grad
-
-        # return trained circuit with final params packed
-        ry_opt, cry_opt = self._unpack_params(circuit, theta)
-        trained = A.EntangledCircuit(qubits=circuit.qubits, ry=ry_opt, edges=circuit.edges, cry=cry_opt, target_p=circuit.target_p, target_cov=circuit.target_cov)
-        return {"circuit": trained, "history": history}
diff --git a/scenario_generation/quantum_hybrid.py b/scenario_generation/quantum_hybrid.py
deleted file mode 100644
index 5ae06cf..0000000
--- a/scenario_generation/quantum_hybrid.py
+++ /dev/null
@@ -1,70 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-from typing import Optional
-
-from systemic_risk.generators.base import ScenarioGenerator
-from systemic_risk.spec import SystemSpec
-
-from .gam_generator import GAMGenerator
-
-
-class HybridGAMGenerator(ScenarioGenerator):
-    """Hybrid generator that mixes GAM (classical) with an entangled Born machine.
-
-    Parameters
-    - quantum_fraction: fraction of samples drawn from the quantum generator (0..1).
-    - entangled_kwargs: passed to EntangledBornMachineGenerator constructor.
-    """
-
-    name = "Hybrid GAM (quantum-classical mix)"
-
-    def __init__(self, quantum_fraction: float = 0.1, *, entangled_kwargs: dict | None = None, augmentation_strength: float = 0.05) -> None:
-        if not (0.0 <= quantum_fraction <= 1.0):
-            raise ValueError("quantum_fraction must lie in [0,1]")
-        self.quantum_fraction = float(quantum_fraction)
-        self.entangled_kwargs = dict(entangled_kwargs or {})
-        self.augmentation_strength = float(augmentation_strength)
-
-        self._quantum: Optional[object] = None
-        self._gam: Optional[GAMGenerator] = None
-        self._spec: Optional[SystemSpec] = None
-
-    def fit(self, spec: SystemSpec) -> None:
-        # Lazy import to avoid heavy quantum deps unless used
-        from systemic_risk.generators import EntangledBornMachineGenerator
-
-        self._spec = spec
-        self._gam = GAMGenerator(augmentation_strength=self.augmentation_strength)
-        self._gam.fit(spec)
-
-        # instantiate and fit the entangled (quantum) generator
-        qargs = dict(self.entangled_kwargs)
-        q = EntangledBornMachineGenerator(**qargs)
-        q.fit(spec)
-        self._quantum = q
-
-    def sample(self, n_samples: int, seed: int | None = None) -> np.ndarray:
-        if self._spec is None or self._gam is None or self._quantum is None:
-            raise RuntimeError("HybridGAMGenerator must be fit before sampling")
-        rng = np.random.default_rng(seed)
-        n_q = int(round(self.quantum_fraction * n_samples))
-        n_c = n_samples - n_q
-
-        parts = []
-        if n_q > 0:
-            q_samples = self._quantum.sample(n_q, seed=rng.integers(0, 2 ** 31))
-            parts.append(q_samples.astype(int))
-        if n_c > 0:
-            c_samples = self._gam.sample(n_c, seed=rng.integers(0, 2 ** 31))
-            parts.append(c_samples.astype(int))
-
-        if len(parts) == 1:
-            combined = parts[0]
-        else:
-            combined = np.vstack(parts)
-            # shuffle rows to mix quantum and classical draws
-            idx = rng.permutation(combined.shape[0])
-            combined = combined[idx]
-
-        return combined.astype(int)
diff --git a/scenario_generation/validation.py b/scenario_generation/validation.py
deleted file mode 100644
index d3257a9..0000000
--- a/scenario_generation/validation.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-from typing import Tuple
-
-from systemic_risk.spec import SystemSpec
-
-
-def validate_marginals(samples: np.ndarray, spec: SystemSpec) -> Tuple[np.ndarray, np.ndarray]:
-    samples = np.asarray(samples, dtype=float)
-    sampled = samples.mean(axis=0)
-    target = np.asarray(spec.marginal_default_probs, dtype=float)
-    return sampled, target
-
-
-def validate_pairwise_corr(samples: np.ndarray, spec: SystemSpec) -> Tuple[np.ndarray, np.ndarray]:
-    samples = np.asarray(samples, dtype=float)
-    n_samples, n = samples.shape
-    sampled_joint = (samples.T @ samples) / max(n_samples, 1)
-    sampled_corr = np.eye(n)
-    sampled_marginals = samples.mean(axis=0)
-    for i in range(n):
-        for j in range(i + 1, n):
-            denom = (sampled_marginals[i] * (1 - sampled_marginals[i]) * sampled_marginals[j] * (1 - sampled_marginals[j])) ** 0.5
-            corr = 0.0 if denom == 0 else (sampled_joint[i, j] - sampled_marginals[i] * sampled_marginals[j]) / denom
-            sampled_corr[i, j] = sampled_corr[j, i] = float(np.clip(corr, -1.0, 1.0))
-
-    # Convert the spec's targets into the Bernoulli pairwise correlation space
-    # (the spec may carry a latent Gaussian correlation; use the joint probs API
-    # which already handles conversion based on metadata).
-    from systemic_risk.spec import joint_to_corr
-
-    target_joint = spec.target_pairwise_joint_probs()
-    target = joint_to_corr(target_joint, np.asarray(spec.marginal_default_probs))
-
-    return sampled_corr, target
-
-
-def validate_schema(samples: np.ndarray, spec: SystemSpec) -> bool:
-    samples = np.asarray(samples)
-    if samples.ndim != 2:
-        return False
-    if samples.shape[1] != spec.n:
-        return False
-    if not np.all(np.isin(samples, [0, 1])):
-        return False
-    return True
-
-
-def validate_scenarios(samples: np.ndarray, spec: SystemSpec) -> dict:
-    sampled_marginals, target_marginals = validate_marginals(samples, spec)
-    sampled_corr, target_corr = validate_pairwise_corr(samples, spec)
-    schema_ok = validate_schema(samples, spec)
-    return {
-        "schema_ok": schema_ok,
-        "sampled_marginals": sampled_marginals,
-        "target_marginals": target_marginals,
-        "sampled_corr": sampled_corr,
-        "target_corr": target_corr,
-    }
diff --git a/scripts/train_qgan.py b/scripts/train_qgan.py
new file mode 100644
index 0000000..3f3c0d8
--- /dev/null
+++ b/scripts/train_qgan.py
@@ -0,0 +1,107 @@
+"""Train the adversarial Born-machine generator (qGAN) on a synthetic spec.
+
+The project's canonical generator uses analytic angles (no training). This
+script exercises the *experimental* adversarial path: a parameterised Born
+machine trained against a small NumPy discriminator, with generator gradients
+estimated by finite differences on the exact statevector.
+
+Run:
+    uv run python scripts/train_qgan.py
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import numpy as np
+
+from systemic_risk.data.synthetic import make_synthetic_system
+from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator
+from systemic_risk.generators.quantum import ansatz as A
+from systemic_risk.generators.quantum.statevector import StateVector, sample_bitstrings
+from scenario_generation.qgan_trainer import QGANTrainer
+
+
+def _generate(circuit: A.EntangledCircuit, n_samples: int, rng) -> np.ndarray:
+    """Sample binary default vectors from a trained block circuit."""
+    sv = StateVector(circuit.size)
+    for i, th in enumerate(circuit.ry):
+        sv.ry(i, float(th))
+    for e, (c, t) in enumerate(circuit.edges):
+        sv.cry(int(c), int(t), float(circuit.cry[e]))
+    bits = sample_bitstrings(sv.probabilities(), circuit.size, n_samples, rng)
+    out = np.zeros((n_samples, max(circuit.qubits) + 1), dtype=int)
+    for col, q in enumerate(circuit.qubits):
+        out[:, q] = bits[:, col]
+    return out
+
+
+def main() -> None:
+    n = 12
+    spec = make_synthetic_system(n=n, seed=7)
+    print(f"Spec: n={spec.n}  marginals(min/mean/max)="
+          f"{spec.marginal_default_probs.min():.3f}/"
+          f"{spec.marginal_default_probs.mean():.3f}/"
+          f"{spec.marginal_default_probs.max():.3f}")
+
+    trainer = QGANTrainer(
+        n_epochs=40,
+        batch_size=256,
+        disc_steps=3,
+        gen_steps=1,
+        lr_disc=5e-2,
+        lr_gen=8e-2,
+        fd_eps=1e-2,
+        seed=0,
+    )
+    print("Training qGAN (finite-difference generator gradients)...")
+    result = trainer.fit(spec, n_real=4000)
+    circuit = result["circuit"]
+    hist = result["history"]
+
+    block = list(circuit.qubits)
+    print(f"Trained block qubits={block}  #params={circuit.size + len(circuit.edges)}")
+    print(f"disc_loss: {hist['disc_loss'][0]:.4f} -> {hist['disc_loss'][-1]:.4f}")
+    print(f"gen_loss:  {hist['gen_loss'][0]:.4f} -> {hist['gen_loss'][-1]:.4f}")
+
+    # --- validation: compare generated vs. copula-target moments on the block ---
+    rng = np.random.default_rng(123)
+    gen = _generate(circuit, 8000, rng)[:, block]
+
+    gauss = GaussianCopulaGenerator()
+    gauss.fit(spec)
+    real = gauss.sample(8000, seed=999)[:, block]
+
+    gen_marg = gen.mean(axis=0)
+    real_marg = real.mean(axis=0)
+    marg_mae = float(np.abs(gen_marg - real_marg).mean())
+
+    gen_corr = np.corrcoef(gen, rowvar=False)
+    real_corr = np.corrcoef(real, rowvar=False)
+    iu = np.triu_indices(len(block), k=1)
+    corr_mae = float(np.abs(gen_corr[iu] - real_corr[iu]).mean())
+
+    print(f"\nValidation on block (vs. Gaussian-copula target):")
+    print(f"  marginal MAE:    {marg_mae:.4f}")
+    print(f"  pairwise corr MAE: {corr_mae:.4f}")
+
+    out_dir = Path("outputs")
+    out_dir.mkdir(exist_ok=True)
+    metrics = {
+        "n": spec.n,
+        "block": block,
+        "n_params": int(circuit.size + len(circuit.edges)),
+        "disc_loss_first": hist["disc_loss"][0],
+        "disc_loss_last": hist["disc_loss"][-1],
+        "gen_loss_first": hist["gen_loss"][0],
+        "gen_loss_last": hist["gen_loss"][-1],
+        "marginal_mae": marg_mae,
+        "corr_mae": corr_mae,
+    }
+    (out_dir / "qgan_training.json").write_text(json.dumps(metrics, indent=2))
+    print(f"\nWrote {out_dir / 'qgan_training.json'}")
+
+
+if __name__ == "__main__":
+    main()

From a49193c3925bd50e3164663e6c99f351e850cfe5 Mon Sep 17 00:00:00 2001
From: Alex Do <alexxdo2004@gmail.com>
Date: Sun, 7 Jun 2026 03:24:34 +0300
Subject: [PATCH 2/5] Strengthen qGAN trainer; validate trained circuit on IBM
 QPU

Trainer (src/scenario_generation/qgan_trainer.py):
- Generator objective evaluated as the EXACT expectation over the Born
  distribution (was Monte-Carlo sampled) -> noise-free; gradient is a central
  finite difference on that exact expectation (hardware analogue: parameter
  shift). Non-saturating objective max E_p[log D]. Adam for both generator and
  (regularised) discriminator. Best-circuit tracking by exact moment error.
- fit() now accepts an optional prebuilt circuit + target samples so a
  hand-built (sparse, hardware-friendly) ansatz can be trained.

Result on the n=12 block: moment error -95.4% vs the analytic seed; marginal
MAE 0.024->0.010, corr MAE 0.089->0.052.

scripts/train_qgan.py: --mode dense (full graph) and --mode hw (inflated
marginals above the QPU noise floor + max-degree-2 chain), saving the trained
hw circuit for submission.

scripts/run_qgan_hardware.py: submit the trained hw circuit to a QPU (dry-run by
default). Validated on ibm_boston (job d8ibjps2upec739m142g, 100k shots): 22
two-qubit gates, 0 SWAPs, 12/12 marginals above the readout floor, marginal RMSE
vs ideal 0.012, chain joint RMSE vs ideal 0.0039.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 scripts/run_qgan_hardware.py            | 153 +++++++++++++++
 scripts/train_qgan.py                   | 163 ++++++++++++----
 src/scenario_generation/qgan_trainer.py | 244 +++++++++++++++++-------
 3 files changed, 460 insertions(+), 100 deletions(-)
 create mode 100644 scripts/run_qgan_hardware.py

diff --git a/scripts/run_qgan_hardware.py b/scripts/run_qgan_hardware.py
new file mode 100644
index 0000000..efb2b77
--- /dev/null
+++ b/scripts/run_qgan_hardware.py
@@ -0,0 +1,153 @@
+"""Submit the trained qGAN circuit to a real IBM QPU and validate it.
+
+The qGAN is trained in exact simulation (``scripts/train_qgan.py --mode hw``);
+training on hardware is infeasible (each finite-difference gradient step is many
+thousands of circuit evaluations). The legitimate hardware step is to take the
+*final* trained circuit and run it on a QPU, comparing hardware-sampled moments
+to the exact statevector.
+
+The saved hw circuit is built to be loadable: inflated marginals (above the
+~2-3% readout floor) on a max-degree-2 chain that pins to a low-error physical
+line with zero routing SWAPs (dynamical decoupling + measurement twirling on).
+
+Dry-run (no cloud job) by default; pass --submit to actually run.
+
+    uv run --extra quantum python scripts/run_qgan_hardware.py            # dry-run
+    uv run --extra quantum python scripts/run_qgan_hardware.py --submit   # real QPU
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+from pathlib import Path
+
+import numpy as np
+
+from systemic_risk.generators.quantum import ansatz as A
+from systemic_risk.generators.quantum.statevector import StateVector
+from systemic_risk.generators.quantum.ibm_runtime import (
+    DEFAULT_HARDWARE_SHOTS,
+    best_qubit_line,
+    run_block,
+)
+
+
+def load_circuit(path: Path) -> A.EntangledCircuit:
+    d = np.load(path)
+    return A.EntangledCircuit(
+        qubits=d["qubits"].tolist(),
+        ry=d["ry"],
+        edges=[tuple(map(int, e)) for e in d["edges"]],
+        cry=d["cry"],
+        target_p=d["target_p"],
+        target_cov=d["target_cov"],
+    )
+
+
+def ideal_moments(circuit: A.EntangledCircuit):
+    sv = StateVector(circuit.size)
+    for i, th in enumerate(circuit.ry):
+        sv.ry(i, float(th))
+    for e, (c, t) in enumerate(circuit.edges):
+        sv.cry(int(c), int(t), float(circuit.cry[e]))
+    return sv.marginals(), sv.pairwise_joint()
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--circuit", type=Path, default=Path("outputs/qgan_hw_circuit.npz"))
+    p.add_argument("--backend", default="ibm_boston")
+    p.add_argument("--shots", type=int, default=DEFAULT_HARDWARE_SHOTS)
+    p.add_argument("--optimization-level", type=int, choices=range(4), default=3)
+    p.add_argument("--submit", action="store_true")
+    p.add_argument("--output-dir", type=Path, default=Path("outputs/ibm_quantum"))
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    circuit = load_circuit(args.circuit)
+    n = circuit.size
+    edges = circuit.edges
+
+    ideal_marg, ideal_joint = ideal_moments(circuit)
+    tgt_marg = circuit.target_p
+    tgt_joint = circuit.target_cov + np.outer(tgt_marg, tgt_marg)
+
+    from qiskit_ibm_runtime import QiskitRuntimeService
+
+    svc = QiskitRuntimeService(
+        token=os.environ.get("IBM_QUANTUM_TOKEN"),
+        channel=os.environ.get("IBM_QUANTUM_CHANNEL", "ibm_quantum_platform"),
+        instance=os.environ.get("IBM_QUANTUM_INSTANCE"),
+    )
+    backend = svc.backend(args.backend)
+    line, ro = best_qubit_line(backend, n)
+
+    # statistical-resolvability budget
+    shot_sigma = np.sqrt(tgt_marg * (1 - tgt_marg) / args.shots)
+    line_floor = float(np.mean(ro[line]))
+    resolvable = [bool(tm > line_floor + 2 * ss) for tm, ss in zip(tgt_marg, shot_sigma)]
+    exp_codefaults = np.array([args.shots * ideal_joint[i, j] for i, j in edges])
+    stats = {
+        "shots": args.shots,
+        "line_readout_floor_pct": round(line_floor * 100, 3),
+        "ideal_marginals_pct": [round(x * 100, 2) for x in ideal_marg.tolist()],
+        "marginals_resolvable_above_floor": f"{sum(resolvable)}/{n}",
+        "expected_codefaults_per_chain_pair": [round(x, 1) for x in exp_codefaults.tolist()],
+        "pairs_with_>=10_codefaults": int((exp_codefaults >= 10).sum()),
+    }
+
+    if not args.submit:
+        print(json.dumps({
+            "status": "dry-run (no cloud job)",
+            "backend": args.backend,
+            "n": n,
+            "chain_edges_local": edges,
+            "physical_line": line,
+            "expected_two_qubit_gates": 2 * len(edges),
+            **stats,
+        }, indent=2))
+        return
+
+    result = run_block(
+        circuit, shots=args.shots, backend_name=args.backend,
+        optimization_level=args.optimization_level, initial_layout=line,
+        dynamical_decoupling=True, measure_twirling=True,
+    )
+    samples = result.samples
+    obs_marg = samples.mean(axis=0)
+    obs_joint = (samples.T @ samples) / len(samples)
+
+    edge_mask = np.zeros((n, n), bool)
+    for i, j in edges:
+        edge_mask[i, j] = edge_mask[j, i] = True
+
+    report = {
+        "source": "trained qGAN circuit (hw mode)",
+        "backend": result.backend_name,
+        "job_id": result.job_id,
+        "physical_line": line,
+        "two_qubit_gates": result.two_qubit_gates,
+        "circuit_depth": result.circuit_depth,
+        "swaps_added": result.two_qubit_gates - 2 * len(edges),
+        **stats,
+        "hardware_marginals_pct": [round(x * 100, 2) for x in obs_marg.tolist()],
+        "marginal_rmse_vs_ideal": float(np.sqrt(np.mean((obs_marg - ideal_marg) ** 2))),
+        "marginal_rmse_vs_target": float(np.sqrt(np.mean((obs_marg - tgt_marg) ** 2))),
+        "joint_rmse_chain_vs_ideal": float(
+            np.sqrt(np.mean((obs_joint[edge_mask] - ideal_joint[edge_mask]) ** 2))
+        ),
+        "hardware_codefaults_on_chain": [int(obs_joint[i, j] * result.shots) for i, j in edges],
+    }
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    stem = f"qgan_hardware_{result.backend_name}_{result.job_id}"
+    np.savez_compressed(args.output_dir / f"{stem}_samples.npz", samples=samples)
+    (args.output_dir / f"{stem}_report.json").write_text(json.dumps(report, indent=2), encoding="utf-8")
+    print(json.dumps(report, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/train_qgan.py b/scripts/train_qgan.py
index 3f3c0d8..ae23cf6 100644
--- a/scripts/train_qgan.py
+++ b/scripts/train_qgan.py
@@ -1,16 +1,28 @@
-"""Train the adversarial Born-machine generator (qGAN) on a synthetic spec.
+"""Train the adversarial Born-machine generator (qGAN) and save the result.
 
 The project's canonical generator uses analytic angles (no training). This
 script exercises the *experimental* adversarial path: a parameterised Born
-machine trained against a small NumPy discriminator, with generator gradients
-estimated by finite differences on the exact statevector.
+machine trained against a NumPy discriminator, with the generator objective
+evaluated as the **exact expectation** over the Born distribution and its
+gradient taken by central differences on that exact expectation (the noise-free
+analogue of the hardware parameter-shift rule).
+
+It trains two configurations:
+
+  --mode dense  (default) the strongest n=12 dependency block, full graph
+  --mode hw     a hardware-friendly chain: inflated marginals (above the QPU
+                noise floor) + a max-degree-2 (near-neighbour) entangler graph,
+                whose trained circuit is saved to outputs/qgan_hw_circuit.npz for
+                submission by scripts/run_qgan_hardware.py.
 
 Run:
-    uv run python scripts/train_qgan.py
+    uv run python scripts/train_qgan.py --mode dense
+    uv run python scripts/train_qgan.py --mode hw
 """
 
 from __future__ import annotations
 
+import argparse
 import json
 from pathlib import Path
 
@@ -24,20 +36,44 @@
 
 
 def _generate(circuit: A.EntangledCircuit, n_samples: int, rng) -> np.ndarray:
-    """Sample binary default vectors from a trained block circuit."""
+    """Sample binary default vectors from a trained block circuit (block columns only)."""
     sv = StateVector(circuit.size)
     for i, th in enumerate(circuit.ry):
         sv.ry(i, float(th))
     for e, (c, t) in enumerate(circuit.edges):
         sv.cry(int(c), int(t), float(circuit.cry[e]))
     bits = sample_bitstrings(sv.probabilities(), circuit.size, n_samples, rng)
-    out = np.zeros((n_samples, max(circuit.qubits) + 1), dtype=int)
-    for col, q in enumerate(circuit.qubits):
-        out[:, q] = bits[:, col]
-    return out
+    return bits  # (n_samples, circuit.size) in block-local order
+
+
+def _chain_order(spec, block):
+    """Order banks so the most strongly-correlated are adjacent (heavy-hex line, 0 SWAPs)."""
+    dep = np.abs(spec.dependency_matrix())
+    np.fill_diagonal(dep, 0.0)
+    remaining = set(block)
+    start = max(block, key=lambda q: dep[q, block].sum())
+    order = [start]
+    remaining.discard(start)
+    while remaining:
+        last = order[-1]
+        nxt = max(remaining, key=lambda q: dep[last, q])
+        order.append(nxt)
+        remaining.discard(nxt)
+    return order
+
+
+def _inflate_marginals(spec, inflate_to=0.10):
+    """Lift PDs above the ~2-3% QPU noise floor (preserving order) for a loadable demo."""
+    p = np.clip(spec.marginal_default_probs, 1e-6, 1 - 1e-6)
+    return np.clip(p / p.max() * inflate_to + 0.03, 0.03, 0.45)
 
 
 def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--mode", choices=["dense", "hw"], default="dense")
+    ap.add_argument("--epochs", type=int, default=150)
+    args = ap.parse_args()
+
     n = 12
     spec = make_synthetic_system(n=n, seed=7)
     print(f"Spec: n={spec.n}  marginals(min/mean/max)="
@@ -45,62 +81,117 @@ def main() -> None:
           f"{spec.marginal_default_probs.mean():.3f}/"
           f"{spec.marginal_default_probs.max():.3f}")
 
+    circuit_seed = None
+    if args.mode == "dense":
+        block = list(range(n))
+        tag = "dense"
+        gauss = GaussianCopulaGenerator()
+        gauss.fit(spec)
+        real = gauss.sample(8000, seed=4242)
+    else:
+        tag = "hw"
+        # Inflate PDs above the noise floor, then draw the target from the SAME
+        # inflated spec so generator target and discriminator data are consistent.
+        order = _chain_order(spec, list(range(n)))
+        block = order
+        p_hw = _inflate_marginals(spec, inflate_to=0.10)
+        spec.marginal_default_probs[:] = p_hw
+        gauss = GaussianCopulaGenerator()
+        gauss.fit(spec)
+        real = gauss.sample(8000, seed=4242)
+        global_edges = [(order[i], order[i + 1]) for i in range(len(order) - 1)]
+        circuit_seed = A._block_circuit(order, p_hw, A.target_covariance(spec), global_edges)
+        print(f"HW chain order: {order}")
+        print(f"HW marginals(min/mean/max)={p_hw.min():.3f}/{p_hw.mean():.3f}/{p_hw.max():.3f}  "
+              f"edges={len(circuit_seed.edges)} (max-degree-2 chain)")
+
     trainer = QGANTrainer(
-        n_epochs=40,
-        batch_size=256,
-        disc_steps=3,
+        n_epochs=args.epochs,
+        batch_size=512,
+        disc_steps=2,
         gen_steps=1,
-        lr_disc=5e-2,
-        lr_gen=8e-2,
+        lr_disc=2e-2,
+        lr_gen=1.2e-1,
         fd_eps=1e-2,
         seed=0,
     )
-    print("Training qGAN (finite-difference generator gradients)...")
-    result = trainer.fit(spec, n_real=4000)
+
+    # analytic-seed baseline moment error (before any training)
+    if circuit_seed is None:
+        edges = A.dependency_edges(spec, threshold=0.02, within_clusters_only=False)
+        circuit_seed = A._block_circuit(
+            block, np.clip(spec.marginal_default_probs, 1e-6, 1 - 1e-6),
+            A.target_covariance(spec), edges,
+        )
+    bt = QGANTrainer._bit_table(circuit_seed.size)
+    seed_err = trainer._moment_error(
+        circuit_seed, trainer._probabilities(circuit_seed, trainer._build_circuit_params(circuit_seed)), bt,
+    )
+
+    print(f"\nTraining qGAN [{tag}] for {args.epochs} epochs "
+          f"({circuit_seed.size} RY + {len(circuit_seed.edges)} CRY params)...")
+    result = trainer.fit(spec, circuit=circuit_seed, real=real)
     circuit = result["circuit"]
     hist = result["history"]
 
-    block = list(circuit.qubits)
-    print(f"Trained block qubits={block}  #params={circuit.size + len(circuit.edges)}")
     print(f"disc_loss: {hist['disc_loss'][0]:.4f} -> {hist['disc_loss'][-1]:.4f}")
-    print(f"gen_loss:  {hist['gen_loss'][0]:.4f} -> {hist['gen_loss'][-1]:.4f}")
+    print(f"gen_obj:   {hist['gen_loss'][0]:.4f} -> {hist['gen_loss'][-1]:.4f}")
+    print(f"moment_err: seed {seed_err:.5f} -> best {hist['best_moment_err']:.5f}  "
+          f"({100*(1-hist['best_moment_err']/max(seed_err,1e-12)):.1f}% reduction)")
 
-    # --- validation: compare generated vs. copula-target moments on the block ---
+    # --- validation: generated vs. copula-target moments on the block ---
     rng = np.random.default_rng(123)
-    gen = _generate(circuit, 8000, rng)[:, block]
-
-    gauss = GaussianCopulaGenerator()
-    gauss.fit(spec)
-    real = gauss.sample(8000, seed=999)[:, block]
+    gen = _generate(circuit, 16000, rng)           # block-local order
+    real_block = real[:, block]                    # same order as circuit.qubits
 
     gen_marg = gen.mean(axis=0)
-    real_marg = real.mean(axis=0)
+    real_marg = real_block.mean(axis=0)
     marg_mae = float(np.abs(gen_marg - real_marg).mean())
 
     gen_corr = np.corrcoef(gen, rowvar=False)
-    real_corr = np.corrcoef(real, rowvar=False)
-    iu = np.triu_indices(len(block), k=1)
+    real_corr = np.corrcoef(real_block, rowvar=False)
+    iu = np.triu_indices(circuit.size, k=1)
     corr_mae = float(np.abs(gen_corr[iu] - real_corr[iu]).mean())
 
+    # adjacent (directly-entangled) pairs -- what a chain circuit can represent
+    adj = [(e[0], e[1]) for e in circuit.edges]
+    adj_mae = float(np.mean([abs(gen_corr[i, j] - real_corr[i, j]) for i, j in adj])) if adj else 0.0
+
     print(f"\nValidation on block (vs. Gaussian-copula target):")
-    print(f"  marginal MAE:    {marg_mae:.4f}")
-    print(f"  pairwise corr MAE: {corr_mae:.4f}")
+    print(f"  marginal MAE:           {marg_mae:.4f}")
+    print(f"  pairwise corr MAE (all):     {corr_mae:.4f}")
+    print(f"  pairwise corr MAE (adjacent): {adj_mae:.4f}  ({len(adj)} directly-entangled pairs)")
 
     out_dir = Path("outputs")
     out_dir.mkdir(exist_ok=True)
     metrics = {
+        "mode": tag,
         "n": spec.n,
-        "block": block,
+        "block": list(map(int, circuit.qubits)),
         "n_params": int(circuit.size + len(circuit.edges)),
-        "disc_loss_first": hist["disc_loss"][0],
+        "epochs": args.epochs,
+        "seed_moment_err": seed_err,
+        "best_moment_err": hist["best_moment_err"],
         "disc_loss_last": hist["disc_loss"][-1],
-        "gen_loss_first": hist["gen_loss"][0],
-        "gen_loss_last": hist["gen_loss"][-1],
+        "gen_obj_last": hist["gen_loss"][-1],
         "marginal_mae": marg_mae,
         "corr_mae": corr_mae,
+        "corr_mae_adjacent": adj_mae,
     }
-    (out_dir / "qgan_training.json").write_text(json.dumps(metrics, indent=2))
-    print(f"\nWrote {out_dir / 'qgan_training.json'}")
+    (out_dir / f"qgan_training_{tag}.json").write_text(json.dumps(metrics, indent=2))
+    print(f"Wrote {out_dir / f'qgan_training_{tag}.json'}")
+
+    if tag == "hw":
+        np.savez(
+            out_dir / "qgan_hw_circuit.npz",
+            qubits=np.array(circuit.qubits, dtype=int),
+            ry=circuit.ry,
+            edges=np.array(circuit.edges, dtype=int),
+            cry=circuit.cry,
+            target_p=circuit.target_p,
+            target_cov=circuit.target_cov,
+        )
+        print(f"Wrote {out_dir / 'qgan_hw_circuit.npz'} (for run_qgan_hardware.py)")
 
 
 if __name__ == "__main__":
diff --git a/src/scenario_generation/qgan_trainer.py b/src/scenario_generation/qgan_trainer.py
index 1fa33c4..60ed57e 100644
--- a/src/scenario_generation/qgan_trainer.py
+++ b/src/scenario_generation/qgan_trainer.py
@@ -11,8 +11,8 @@
 class SimpleDiscriminator:
     """Small feedforward discriminator implemented in NumPy.
 
-    Binary classifier returning probability P(real). Trained with SGD on
-    cross-entropy.
+    Binary classifier returning probability P(real). Trained with the Adam
+    optimiser on cross-entropy.
     """
 
     def __init__(self, input_dim: int, hidden: int = 32, seed: int | None = None):
@@ -21,6 +21,10 @@ def __init__(self, input_dim: int, hidden: int = 32, seed: int | None = None):
         self.b1 = np.zeros(hidden)
         self.W2 = rng.normal(scale=0.1, size=(hidden, 1))
         self.b2 = np.zeros(1)
+        # Adam moment buffers, keyed by parameter id
+        self._m: dict[int, np.ndarray] = {}
+        self._v: dict[int, np.ndarray] = {}
+        self._t = 0
 
     def forward(self, X: np.ndarray) -> np.ndarray:
         h = np.tanh(X @ self.W1 + self.b1)
@@ -28,7 +32,26 @@ def forward(self, X: np.ndarray) -> np.ndarray:
         probs = 1.0 / (1.0 + np.exp(-logits))
         return probs.ravel()
 
-    def train_step(self, X_real: np.ndarray, X_fake: np.ndarray, lr: float = 1e-2) -> float:
+    def _adam_step(self, params: list[np.ndarray], grads: list[np.ndarray], lr: float,
+                   b1: float = 0.9, b2: float = 0.999, eps: float = 1e-8) -> None:
+        self._t += 1
+        for p, g in zip(params, grads):
+            key = id(p)
+            m = self._m.get(key)
+            v = self._v.get(key)
+            if m is None:
+                m = np.zeros_like(p)
+                v = np.zeros_like(p)
+            m = b1 * m + (1.0 - b1) * g
+            v = b2 * v + (1.0 - b2) * (g * g)
+            mhat = m / (1.0 - b1 ** self._t)
+            vhat = v / (1.0 - b2 ** self._t)
+            p -= lr * mhat / (np.sqrt(vhat) + eps)
+            self._m[key] = m
+            self._v[key] = v
+
+    def train_step(self, X_real: np.ndarray, X_fake: np.ndarray, lr: float = 1e-2,
+                   l2: float = 1e-4) -> float:
         X = np.vstack([X_real, X_fake])
         y = np.concatenate([np.ones(X_real.shape[0]), np.zeros(X_fake.shape[0])])
         probs = self.forward(X)
@@ -39,18 +62,19 @@ def train_step(self, X_real: np.ndarray, X_fake: np.ndarray, lr: float = 1e-2) -
         # gradients via simple backprop
         h = np.tanh(X @ self.W1 + self.b1)
         dlogits = (probs - y)[:, None] / X.shape[0]
-        dW2 = h.T @ dlogits
+        dW2 = h.T @ dlogits + l2 * self.W2
         db2 = dlogits.sum(axis=0)
         dh = dlogits @ self.W2.T
         drelu = (1.0 - h ** 2) * dh
-        dW1 = X.T @ drelu
+        dW1 = X.T @ drelu + l2 * self.W1
         db1 = drelu.sum(axis=0)
 
-        # SGD step
-        self.W2 -= lr * dW2
-        self.b2 -= lr * db2
-        self.W1 -= lr * dW1
-        self.b1 -= lr * db1
+        # Adam step (mutates weights in place)
+        self._adam_step(
+            [self.W1, self.b1, self.W2, self.b2],
+            [dW1, db1, dW2, db2],
+            lr,
+        )
         return float(loss)
 
 
@@ -58,10 +82,16 @@ class QGANTrainer:
     """Adversarial trainer for a parameterised Born machine using a NumPy discriminator.
 
     Notes:
-    - This is a simple, small-scale qGAN suitable for statevector-backed training.
-    - Generator parameters are the block `RY` and `CRY` angles from `ansatz._block_circuit`.
-    - Generator gradients are estimated by forward finite differences on the exact
-      statevector-based expectation of the discriminator loss.
+    - This is a small-scale qGAN suitable for statevector-backed training.
+    - Generator parameters are the block ``RY`` and ``CRY`` angles from
+      ``ansatz._block_circuit``.
+    - The generator objective is evaluated as the **exact expectation** over the
+      Born distribution (``sv.probabilities()``), so it carries no Monte-Carlo
+      noise; its gradient is a deterministic **central finite difference** on
+      that exact expectation. (On hardware the same gradient would be obtained by
+      the parameter-shift rule from sampled expectations.) The generator uses the
+      **non-saturating** objective ``max E_p[log D(fake)]`` and an Adam optimiser.
+    - The discriminator is trained on sampled real/fake batches with Adam.
     """
 
     def __init__(
@@ -93,47 +123,121 @@ def _unpack_params(self, circuit: A.EntangledCircuit, params: np.ndarray) -> Tup
         cry = params[n : n + len(circuit.edges)]
         return ry, cry
 
-    def _sample_from_params(self, circuit: A.EntangledCircuit, params: np.ndarray, n_samples: int) -> np.ndarray:
+    def _clip_params(self, circuit: A.EntangledCircuit, params: np.ndarray) -> np.ndarray:
+        """Keep RY in (0, pi) and CRY in a sane range so Adam cannot drift off-manifold."""
+        n = circuit.size
+        out = params.copy()
+        out[:n] = np.clip(out[:n], 1e-6, np.pi - 1e-6)
+        out[n:] = np.clip(out[n:], -2.5, 2.5)
+        return out
+
+    def _probabilities(self, circuit: A.EntangledCircuit, params: np.ndarray) -> np.ndarray:
+        """Exact Born probabilities over the 2^size block basis (flat, C-order)."""
         ry, cry = self._unpack_params(circuit, params)
         sv = StateVector(circuit.size)
         for i, th in enumerate(ry):
             sv.ry(i, float(th))
         for e, (cidx, tidx) in enumerate(circuit.edges):
             sv.cry(int(cidx), int(tidx), float(cry[e]))
-        probs = sv.probabilities()
+        return sv.probabilities()
+
+    @staticmethod
+    def _bit_table(size: int) -> np.ndarray:
+        """(2^size, size) table of basis-state bits, matching ``sample_bitstrings``.
+
+        Qubit ``i`` lives at bit position ``size - 1 - i`` of the flat index.
+        """
+        idx = np.arange(1 << size, dtype=np.uint64)
+        pos = (size - 1 - np.arange(size)).astype(np.uint64)
+        return ((idx[:, None] >> pos[None, :]) & np.uint64(1)).astype(int)
+
+    def _sample_from_params(self, circuit: A.EntangledCircuit, params: np.ndarray, n_samples: int) -> np.ndarray:
+        probs = self._probabilities(circuit, params)
         bits = sample_bitstrings(probs, circuit.size, n_samples, self.rng)
-        # map to global indices: produce full-length vector with zeros then fill qubits
         out = np.zeros((n_samples, max(circuit.qubits) + 1), dtype=int)
         for col, q in enumerate(circuit.qubits):
             out[:, q] = bits[:, col]
         return out
 
-    def fit(self, spec, *, n_real: int = 2000, block_qubits: list[int] | None = None):
-        # construct real samples via Gaussian copula baseline (moment-matched)
-        gauss = GaussianCopulaGenerator()
-        gauss.fit(spec)
-        real = gauss.sample(n_real, seed=self.rng.integers(1 << 30))
-
-        # choose one analytic block circuit to train (small systems / demo)
-        edges = A.dependency_edges(spec, threshold=0.02, within_clusters_only=False)
-        blocks = A.partition_blocks(spec, edges, max_block=spec.n)
-        # pick smallest block with size <= 12 for tractability
-        chosen = None
-        for b in blocks:
-            if len(b) <= 12:
-                chosen = b
-                break
-        if chosen is None:
-            chosen = blocks[0]
-        circuit = A._block_circuit(chosen, np.clip(spec.marginal_default_probs, 1e-6, 1 - 1e-6), A.target_covariance(spec), edges)
-
-        # initial params
-        theta = self._build_circuit_params(circuit)
-        disc = SimpleDiscriminator(input_dim=spec.n, hidden=32, seed=int(self.rng.integers(1 << 30)))
-
-        history = {"disc_loss": [], "gen_loss": []}
+    def _exact_moments(self, probs: np.ndarray, bit_table: np.ndarray):
+        """Exact block marginals and pairwise-joint matrix from the probability vector."""
+        marg = bit_table.T @ probs
+        joint = (bit_table * probs[:, None]).T @ bit_table
+        return marg, joint
+
+    def _moment_error(self, circuit: A.EntangledCircuit, probs: np.ndarray, bit_table: np.ndarray) -> float:
+        marg, joint = self._exact_moments(probs, bit_table)
+        tp = circuit.target_p
+        tj = circuit.target_cov + np.outer(tp, tp)
+        iu = np.triu_indices(circuit.size, k=1)
+        return float(np.sum((marg - tp) ** 2) + np.sum((joint[iu] - tj[iu]) ** 2))
+
+    def fit(
+        self,
+        spec,
+        *,
+        n_real: int = 2000,
+        block_qubits: list[int] | None = None,
+        circuit: A.EntangledCircuit | None = None,
+        real: np.ndarray | None = None,
+    ):
+        """Adversarially train one block circuit.
+
+        ``circuit`` and ``real`` can be supplied to drive a hand-built (e.g.
+        sparse, hardware-friendly) ansatz and custom target samples; otherwise a
+        Gaussian-copula baseline and the strongest dependency block are used.
+        """
+        # real (target) samples -- Gaussian copula baseline unless supplied
+        if real is None:
+            gauss = GaussianCopulaGenerator()
+            gauss.fit(spec)
+            real = gauss.sample(n_real, seed=self.rng.integers(1 << 30))
+
+        # generator circuit -- strongest tractable dependency block unless supplied
+        if circuit is None:
+            edges = A.dependency_edges(spec, threshold=0.02, within_clusters_only=False)
+            blocks = A.partition_blocks(spec, edges, max_block=spec.n)
+            chosen = None
+            for b in blocks:
+                if len(b) <= 12:
+                    chosen = b
+                    break
+            if chosen is None:
+                chosen = blocks[0]
+            circuit = A._block_circuit(
+                chosen,
+                np.clip(spec.marginal_default_probs, 1e-6, 1 - 1e-6),
+                A.target_covariance(spec),
+                edges,
+            )
+
+        block = list(circuit.qubits)
+        bit_table = self._bit_table(circuit.size)
+        # design matrix: full-width (discriminator input) basis vectors, block columns filled
+        full_width = real.shape[1]
+        X_states = np.zeros((1 << circuit.size, full_width), dtype=float)
+        for col, q in enumerate(block):
+            if q < full_width:
+                X_states[:, q] = bit_table[:, col]
+
+        theta = self._clip_params(circuit, self._build_circuit_params(circuit))
+        disc = SimpleDiscriminator(input_dim=full_width, hidden=32, seed=int(self.rng.integers(1 << 30)))
+
+        # generator Adam state
+        gm = np.zeros_like(theta)
+        gv = np.zeros_like(theta)
+        gt = 0
+        b1, b2, adam_eps = 0.9, 0.999, 1e-8
+
+        history = {"disc_loss": [], "gen_loss": [], "moment_err": []}
+        best_theta = theta.copy()
+        best_err = self._moment_error(circuit, self._probabilities(circuit, theta), bit_table)
+        history["moment_err"].append(best_err)
+
+        eps = 1e-12
         for epoch in range(self.n_epochs):
-            # discriminator steps
+            # ---- discriminator steps (sampled batches) ----
+            dl = 0.0
             for _ in range(self.disc_steps):
                 idx = self.rng.integers(0, real.shape[0], size=self.batch_size)
                 Xr = real[idx]
@@ -141,30 +245,42 @@ def fit(self, spec, *, n_real: int = 2000, block_qubits: list[int] | None = None
                 dl = disc.train_step(Xr, Xf, lr=self.lr_disc)
             history["disc_loss"].append(dl)
 
-            # generator steps (finite-difference gradient estimate)
+            # ---- generator steps (exact-expectation, central-difference grad) ----
             for _ in range(self.gen_steps):
-                # approximate loss = mean log(1 - D(x)) over samples
-                Xf = self._sample_from_params(circuit, theta, self.batch_size)
-                probs = disc.forward(Xf)
-                eps = 1e-12
-                gen_loss = float(np.mean(np.log(1.0 - probs + eps)))
-                history["gen_loss"].append(gen_loss)
+                probs = self._probabilities(circuit, theta)
+                d = disc.forward(X_states)
+                g = np.log(d + eps)  # non-saturating: maximise E_p[log D]
+                gen_obj = float(probs @ g)
+                history["gen_loss"].append(gen_obj)
 
-                # finite-difference gradient
                 grad = np.zeros_like(theta)
-                base_loss = gen_loss
                 for i in range(theta.size):
-                    theta_p = theta.copy()
-                    theta_p[i] += self.fd_eps
-                    Xp = self._sample_from_params(circuit, theta_p, self.batch_size)
-                    pp = disc.forward(Xp)
-                    loss_p = float(np.mean(np.log(1.0 - pp + eps)))
-                    grad[i] = (loss_p - base_loss) / self.fd_eps
-
-                # gradient ascent (we maximize log(1-D) to fool discriminator); use lr_gen
-                theta = theta + self.lr_gen * grad
-
-        # return trained circuit with final params packed
-        ry_opt, cry_opt = self._unpack_params(circuit, theta)
-        trained = A.EntangledCircuit(qubits=circuit.qubits, ry=ry_opt, edges=circuit.edges, cry=cry_opt, target_p=circuit.target_p, target_cov=circuit.target_cov)
+                    tp_ = theta.copy(); tp_[i] += self.fd_eps
+                    tm_ = theta.copy(); tm_[i] -= self.fd_eps
+                    pp = self._probabilities(circuit, self._clip_params(circuit, tp_))
+                    pm = self._probabilities(circuit, self._clip_params(circuit, tm_))
+                    grad[i] = ((pp - pm) @ g) / (2.0 * self.fd_eps)
+
+                # Adam ascent (maximise gen_obj)
+                gt += 1
+                gm = b1 * gm + (1.0 - b1) * grad
+                gv = b2 * gv + (1.0 - b2) * (grad * grad)
+                mhat = gm / (1.0 - b1 ** gt)
+                vhat = gv / (1.0 - b2 ** gt)
+                theta = theta + self.lr_gen * mhat / (np.sqrt(vhat) + adam_eps)
+                theta = self._clip_params(circuit, theta)
+
+            err = self._moment_error(circuit, self._probabilities(circuit, theta), bit_table)
+            history["moment_err"].append(err)
+            if err < best_err:
+                best_err = err
+                best_theta = theta.copy()
+
+        # return best circuit by exact moment error
+        ry_opt, cry_opt = self._unpack_params(circuit, best_theta)
+        trained = A.EntangledCircuit(
+            qubits=circuit.qubits, ry=ry_opt, edges=circuit.edges, cry=cry_opt,
+            target_p=circuit.target_p, target_cov=circuit.target_cov,
+        )
+        history["best_moment_err"] = best_err
         return {"circuit": trained, "history": history}

From 1fd92681b30487faa979325700a141fd7c425a0e Mon Sep 17 00:00:00 2001
From: Alex Do <alexxdo2004@gmail.com>
Date: Sun, 7 Jun 2026 03:46:10 +0300
Subject: [PATCH 3/5] Add cross-source scenario dataset generator

scripts/build_dataset.py samples correlated binary default scenarios from every
generator, side-by-side, across the real G-SIB network and the synthetic network
specs (16- and 54-node). Writes per source/generator CSV + compressed NPZ, the
SystemSpec (spec.json), per-generator fit quality (moments.json), and a top-level
manifest.json indexing shapes, seeds and moment errors.

Generators: Bernoulli, Gaussian copula, Student-t copula, entangled Born machine.
IsingBoltzmann is excluded -- it freezes into a degenerate all-default state on
sparse low-PD networks (real-net marginal RMSE ~1.0). A degeneracy flag in
moments.json guards the remaining generators.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 scripts/build_dataset.py | 170 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 170 insertions(+)
 create mode 100644 scripts/build_dataset.py

diff --git a/scripts/build_dataset.py b/scripts/build_dataset.py
new file mode 100644
index 0000000..f19f968
--- /dev/null
+++ b/scripts/build_dataset.py
@@ -0,0 +1,170 @@
+"""Generate a labelled scenario dataset across data sources and generators.
+
+For each data source (the real G-SIB network plus the synthetic network specs)
+this samples correlated binary default scenarios from every generator and writes
+them side-by-side, so the files can be used to train/benchmark generators or to
+compare classical vs quantum samplers on a common footing.
+
+Layout (under --out-dir, default outputs/dataset/):
+
+    <source>/
+        spec.json                      # the SystemSpec (marginals, correlation, ...)
+        <source>__<generator>.csv      # one row per scenario, header = institutions
+        <source>__<generator>.npz      # samples + node_names + target marginals/corr
+        moments.json                   # per-generator fit quality (RMSE vs targets)
+    manifest.json                      # full index: shapes, seeds, moment errors
+
+Run:
+    uv run python scripts/build_dataset.py                 # defaults
+    uv run python scripts/build_dataset.py --n-samples 8000
+    uv run python scripts/build_dataset.py --sources real synthetic_16
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import time
+from pathlib import Path
+
+import numpy as np
+
+from systemic_risk.data.synthetic import make_synthetic_system, make_scalable_system
+from systemic_risk.data_network.assemble import build_system_spec
+
+from systemic_risk.generators.bernoulli import BernoulliGenerator
+from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator
+from systemic_risk.generators.student_t_copula import StudentTCopulaGenerator
+from systemic_risk.generators.quantum_born_machine import EntangledBornMachineGenerator
+
+from scenario_generation.io import save_scenarios
+
+
+# data sources: real network + the genuinely-synthetic network specs
+# (clustered / Huang are excluded -- they are synthetic stylised models)
+SOURCES: dict[str, tuple[str, callable]] = {
+    "real_gsib": ("real", lambda: build_system_spec()),
+    "synthetic_16": ("synthetic", lambda: make_synthetic_system(n=16, seed=7)),
+    "synthetic_54": ("synthetic", lambda: make_scalable_system(n=54, seed=11)),
+}
+
+# generators sampled side-by-side; each is a fresh instance per source.
+# (IsingBoltzmannGenerator is excluded: it freezes into a degenerate all-default
+# state on sparse, low-PD networks like the real G-SIB net.)
+GENERATORS: dict[str, callable] = {
+    "bernoulli": lambda: BernoulliGenerator(),
+    "gaussian_copula": lambda: GaussianCopulaGenerator(),
+    "student_t_copula": lambda: StudentTCopulaGenerator(df=4.0),
+    "entangled_born_machine": lambda: EntangledBornMachineGenerator(),
+}
+
+
+def moment_errors(samples: np.ndarray, spec) -> dict:
+    """Fit quality + validity flags for one generator's samples.
+
+    ``degenerate`` marks samples that have collapsed (e.g. an Ising model frozen
+    into its ordered all-default phase): the marginals are wildly off target or
+    the support has collapsed to a handful of distinct scenarios.
+    """
+    emp_marg = samples.mean(axis=0)
+    tgt_marg = spec.marginal_default_probs
+    marg_rmse = float(np.sqrt(np.mean((emp_marg - tgt_marg) ** 2)))
+    n_unique = int(np.unique(samples, axis=0).shape[0])
+
+    out = {
+        "marginal_rmse": marg_rmse,
+        "mean_default_rate": float(emp_marg.mean()),
+        "n_unique_scenarios": n_unique,
+    }
+    if spec.target_pairwise_corr is not None and samples.shape[0] > 1:
+        with np.errstate(invalid="ignore"):
+            emp_corr = np.corrcoef(samples, rowvar=False)
+        emp_corr = np.nan_to_num(emp_corr)
+        iu = np.triu_indices(spec.n, k=1)
+        corr_rmse = float(np.sqrt(np.mean((emp_corr[iu] - spec.target_pairwise_corr[iu]) ** 2)))
+        out["corr_rmse"] = corr_rmse
+    # collapsed support, or marginals an order of magnitude off the target scale
+    out["degenerate"] = bool(marg_rmse > 0.10 or n_unique < max(10, samples.shape[0] // 100))
+    return out
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--n-samples", type=int, default=4000)
+    ap.add_argument("--seed", type=int, default=12345)
+    ap.add_argument("--sources", nargs="*", default=list(SOURCES), choices=list(SOURCES))
+    ap.add_argument("--generators", nargs="*", default=list(GENERATORS), choices=list(GENERATORS))
+    ap.add_argument("--out-dir", type=Path, default=Path("outputs/dataset"))
+    args = ap.parse_args()
+
+    args.out_dir.mkdir(parents=True, exist_ok=True)
+    manifest = {
+        "n_samples": args.n_samples,
+        "seed": args.seed,
+        "sources": {},
+    }
+
+    for src_name in args.sources:
+        kind, builder = SOURCES[src_name]
+        print(f"\n=== {src_name} ({kind}) ===")
+        spec = builder()
+        src_dir = args.out_dir / src_name
+        src_dir.mkdir(parents=True, exist_ok=True)
+        spec.save_json(src_dir / "spec.json")
+
+        src_entry = {
+            "kind": kind,
+            "n_institutions": int(spec.n),
+            "node_names": list(spec.node_names),
+            "marginals_mean": float(spec.marginal_default_probs.mean()),
+            "generators": {},
+        }
+        moments = {}
+
+        for gen_name in args.generators:
+            try:
+                gen = GENERATORS[gen_name]()
+                t0 = time.perf_counter()
+                gen.fit(spec)
+                samples = np.asarray(gen.sample(args.n_samples, seed=args.seed), dtype=int)
+                dt = time.perf_counter() - t0
+
+                stem = f"{src_name}__{gen_name}"
+                save_scenarios(src_dir / f"{stem}.csv", samples, spec.node_names)
+                np.savez_compressed(
+                    src_dir / f"{stem}.npz",
+                    samples=samples,
+                    node_names=np.array(spec.node_names),
+                    target_marginals=spec.marginal_default_probs,
+                    target_corr=(spec.target_pairwise_corr
+                                 if spec.target_pairwise_corr is not None
+                                 else np.empty((0, 0))),
+                    source=src_name,
+                    generator=gen_name,
+                    seed=args.seed,
+                )
+                err = moment_errors(samples, spec)
+                moments[gen_name] = err
+                src_entry["generators"][gen_name] = {
+                    "shape": list(samples.shape),
+                    "fit_sample_seconds": round(dt, 2),
+                    **err,
+                }
+                flag = "  [DEGENERATE]" if err.get("degenerate") else ""
+                msg = (f"marg_rmse={err['marginal_rmse']:.4f} "
+                       f"corr_rmse={err.get('corr_rmse', float('nan')):.4f} "
+                       f"uniq={err['n_unique_scenarios']}")
+                print(f"  {gen_name:24s} {samples.shape}  {msg}  ({dt:.1f}s){flag}")
+            except Exception as e:  # noqa: BLE001 -- record and continue
+                src_entry["generators"][gen_name] = {"error": repr(e)}
+                print(f"  {gen_name:24s} FAILED: {e!r}")
+
+        (src_dir / "moments.json").write_text(json.dumps(moments, indent=2))
+        manifest["sources"][src_name] = src_entry
+
+    (args.out_dir / "manifest.json").write_text(json.dumps(manifest, indent=2))
+    print(f"\nWrote dataset to {args.out_dir}/  (manifest.json indexes everything)")
+
+
+if __name__ == "__main__":
+    main()

From 5653c5de7df854aef8765e4454715f1f70ee1286 Mon Sep 17 00:00:00 2001
From: Alex Do <alexxdo2004@gmail.com>
Date: Sun, 7 Jun 2026 03:55:31 +0300
Subject: [PATCH 4/5] Commit generated scenario dataset to tracked path

Add data/scenario_dataset/ (3 sources x 4 generators, 4000 samples each, CSV +
NPZ) plus a README documenting provenance and the rare-event caveat on the real
network. Point build_dataset.py's default --out-dir at the tracked path so
regeneration (deterministic, seed=12345) overwrites the committed copy.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 data/scenario_dataset/README.md               |   50 +
 data/scenario_dataset/manifest.json           |  285 +
 data/scenario_dataset/real_gsib/moments.json  |   30 +
 .../real_gsib/real_gsib__bernoulli.csv        | 4001 +++++++
 .../real_gsib/real_gsib__bernoulli.npz        |  Bin 0 -> 11583 bytes
 .../real_gsib__entangled_born_machine.csv     | 4001 +++++++
 .../real_gsib__entangled_born_machine.npz     |  Bin 0 -> 11395 bytes
 .../real_gsib/real_gsib__gaussian_copula.csv  | 4001 +++++++
 .../real_gsib/real_gsib__gaussian_copula.npz  |  Bin 0 -> 11450 bytes
 .../real_gsib/real_gsib__student_t_copula.csv | 4001 +++++++
 .../real_gsib/real_gsib__student_t_copula.npz |  Bin 0 -> 11417 bytes
 data/scenario_dataset/real_gsib/spec.json     | 3575 +++++++
 .../synthetic_16/moments.json                 |   30 +
 data/scenario_dataset/synthetic_16/spec.json  |  679 ++
 .../synthetic_16/synthetic_16__bernoulli.csv  | 4001 +++++++
 .../synthetic_16/synthetic_16__bernoulli.npz  |  Bin 0 -> 6862 bytes
 .../synthetic_16__entangled_born_machine.csv  | 4001 +++++++
 .../synthetic_16__entangled_born_machine.npz  |  Bin 0 -> 6186 bytes
 .../synthetic_16__gaussian_copula.csv         | 4001 +++++++
 .../synthetic_16__gaussian_copula.npz         |  Bin 0 -> 5978 bytes
 .../synthetic_16__student_t_copula.csv        | 4001 +++++++
 .../synthetic_16__student_t_copula.npz        |  Bin 0 -> 5895 bytes
 .../synthetic_54/moments.json                 |   30 +
 data/scenario_dataset/synthetic_54/spec.json  | 9466 +++++++++++++++++
 .../synthetic_54/synthetic_54__bernoulli.csv  | 4001 +++++++
 .../synthetic_54/synthetic_54__bernoulli.npz  |  Bin 0 -> 12799 bytes
 .../synthetic_54__entangled_born_machine.csv  | 4001 +++++++
 .../synthetic_54__entangled_born_machine.npz  |  Bin 0 -> 12744 bytes
 .../synthetic_54__gaussian_copula.csv         | 4001 +++++++
 .../synthetic_54__gaussian_copula.npz         |  Bin 0 -> 12865 bytes
 .../synthetic_54__student_t_copula.csv        | 4001 +++++++
 .../synthetic_54__student_t_copula.npz        |  Bin 0 -> 12443 bytes
 scripts/build_dataset.py                      |    4 +-
 33 files changed, 62159 insertions(+), 2 deletions(-)
 create mode 100644 data/scenario_dataset/README.md
 create mode 100644 data/scenario_dataset/manifest.json
 create mode 100644 data/scenario_dataset/real_gsib/moments.json
 create mode 100644 data/scenario_dataset/real_gsib/real_gsib__bernoulli.csv
 create mode 100644 data/scenario_dataset/real_gsib/real_gsib__bernoulli.npz
 create mode 100644 data/scenario_dataset/real_gsib/real_gsib__entangled_born_machine.csv
 create mode 100644 data/scenario_dataset/real_gsib/real_gsib__entangled_born_machine.npz
 create mode 100644 data/scenario_dataset/real_gsib/real_gsib__gaussian_copula.csv
 create mode 100644 data/scenario_dataset/real_gsib/real_gsib__gaussian_copula.npz
 create mode 100644 data/scenario_dataset/real_gsib/real_gsib__student_t_copula.csv
 create mode 100644 data/scenario_dataset/real_gsib/real_gsib__student_t_copula.npz
 create mode 100644 data/scenario_dataset/real_gsib/spec.json
 create mode 100644 data/scenario_dataset/synthetic_16/moments.json
 create mode 100644 data/scenario_dataset/synthetic_16/spec.json
 create mode 100644 data/scenario_dataset/synthetic_16/synthetic_16__bernoulli.csv
 create mode 100644 data/scenario_dataset/synthetic_16/synthetic_16__bernoulli.npz
 create mode 100644 data/scenario_dataset/synthetic_16/synthetic_16__entangled_born_machine.csv
 create mode 100644 data/scenario_dataset/synthetic_16/synthetic_16__entangled_born_machine.npz
 create mode 100644 data/scenario_dataset/synthetic_16/synthetic_16__gaussian_copula.csv
 create mode 100644 data/scenario_dataset/synthetic_16/synthetic_16__gaussian_copula.npz
 create mode 100644 data/scenario_dataset/synthetic_16/synthetic_16__student_t_copula.csv
 create mode 100644 data/scenario_dataset/synthetic_16/synthetic_16__student_t_copula.npz
 create mode 100644 data/scenario_dataset/synthetic_54/moments.json
 create mode 100644 data/scenario_dataset/synthetic_54/spec.json
 create mode 100644 data/scenario_dataset/synthetic_54/synthetic_54__bernoulli.csv
 create mode 100644 data/scenario_dataset/synthetic_54/synthetic_54__bernoulli.npz
 create mode 100644 data/scenario_dataset/synthetic_54/synthetic_54__entangled_born_machine.csv
 create mode 100644 data/scenario_dataset/synthetic_54/synthetic_54__entangled_born_machine.npz
 create mode 100644 data/scenario_dataset/synthetic_54/synthetic_54__gaussian_copula.csv
 create mode 100644 data/scenario_dataset/synthetic_54/synthetic_54__gaussian_copula.npz
 create mode 100644 data/scenario_dataset/synthetic_54/synthetic_54__student_t_copula.csv
 create mode 100644 data/scenario_dataset/synthetic_54/synthetic_54__student_t_copula.npz

diff --git a/data/scenario_dataset/README.md b/data/scenario_dataset/README.md
new file mode 100644
index 0000000..816c7f8
--- /dev/null
+++ b/data/scenario_dataset/README.md
@@ -0,0 +1,50 @@
+# Scenario dataset
+
+Correlated binary default scenarios sampled from every generator, side-by-side,
+across one real and two synthetic systems. Generated by
+[`scripts/build_dataset.py`](../../scripts/build_dataset.py) and committed here so
+it can be used without re-running the samplers.
+
+Regenerate (deterministic — same seed reproduces these files):
+
+```bash
+uv run python scripts/build_dataset.py            # -> data/scenario_dataset/
+```
+
+## Contents
+
+`n_samples = 4000`, `seed = 12345`.
+
+| source | kind | institutions | description |
+|---|---|---|---|
+| `real_gsib` | real | 38 | real G-SIB network + non-financial corporates (`build_system_spec`) |
+| `synthetic_16` | synthetic | 16 | community-structured synthetic net (`make_synthetic_system`) |
+| `synthetic_54` | synthetic | 54 | scale-free, ratings-based net, the n=54 hardware target (`make_scalable_system`) |
+
+Generators (one file each per source): `bernoulli`, `gaussian_copula`,
+`student_t_copula` (df=4), `entangled_born_machine`. The classical Ising
+generator is intentionally excluded — it freezes into a degenerate all-default
+state on sparse, low-PD networks.
+
+Per source directory:
+
+```
+<source>/
+    spec.json                      # the SystemSpec (marginals, correlation, names, ...)
+    <source>__<generator>.csv      # 4000 rows; one binary scenario per row; header = institutions
+    <source>__<generator>.npz      # samples + node_names + target marginals/corr + seed
+    moments.json                   # per-generator fit quality + degeneracy flag
+manifest.json                      # top-level index: shapes, seeds, moment errors
+```
+
+Each row is a scenario: `0` = institution survives, `1` = institution defaults.
+
+## Notes
+
+- On `real_gsib` the pairwise-correlation RMSE is high (~0.4) for **all**
+  generators. This is the rare-event wall, not a generator fault: at ~0.2%
+  default probabilities a co-default is essentially never observed in 4000
+  samples, so empirical correlation is dominated by noise. (This is the
+  motivation for the QAE tail-risk estimator.)
+- `moments.json` carries a `degenerate` flag per generator (collapsed support or
+  marginals far off target) as a guard.
diff --git a/data/scenario_dataset/manifest.json b/data/scenario_dataset/manifest.json
new file mode 100644
index 0000000..6572555
--- /dev/null
+++ b/data/scenario_dataset/manifest.json
@@ -0,0 +1,285 @@
+{
+  "n_samples": 4000,
+  "seed": 12345,
+  "sources": {
+    "real_gsib": {
+      "kind": "real",
+      "n_institutions": 38,
+      "node_names": [
+        "JPMorgan Chase",
+        "Bank of America",
+        "Citigroup",
+        "Wells Fargo",
+        "Goldman Sachs",
+        "Morgan Stanley",
+        "U.S. Bancorp",
+        "PNC Financial",
+        "Truist Financial",
+        "Bank of New York Mellon",
+        "State Street",
+        "HSBC Holdings",
+        "Barclays",
+        "Lloyds Banking Group",
+        "Deutsche Bank",
+        "Banco Santander",
+        "Banco Bilbao Vizcaya Argentaria",
+        "ING Groep",
+        "UBS Group",
+        "Mitsubishi UFJ Financial Group",
+        "Sumitomo Mitsui Financial Group",
+        "Mizuho Financial Group",
+        "Nomura Holdings",
+        "Royal Bank of Canada",
+        "Toronto-Dominion Bank",
+        "Bank of Nova Scotia",
+        "Itau Unibanco",
+        "Banco Bradesco",
+        "Apple",
+        "Microsoft",
+        "ExxonMobil",
+        "Walmart",
+        "Boeing",
+        "AT&T",
+        "Toyota Motor",
+        "Volkswagen",
+        "Siemens",
+        "Petrobras"
+      ],
+      "marginals_mean": 0.0023318421052631578,
+      "generators": {
+        "bernoulli": {
+          "shape": [
+            4000,
+            38
+          ],
+          "fit_sample_seconds": 0.0,
+          "marginal_rmse": 0.0006052424481582826,
+          "mean_default_rate": 0.0023947368421052633,
+          "n_unique_scenarios": 54,
+          "corr_rmse": 0.46893489166667135,
+          "degenerate": false
+        },
+        "gaussian_copula": {
+          "shape": [
+            4000,
+            38
+          ],
+          "fit_sample_seconds": 0.04,
+          "marginal_rmse": 0.0008746442885990552,
+          "mean_default_rate": 0.0022500000000000003,
+          "n_unique_scenarios": 72,
+          "corr_rmse": 0.44637849208606306,
+          "degenerate": false
+        },
+        "student_t_copula": {
+          "shape": [
+            4000,
+            38
+          ],
+          "fit_sample_seconds": 24.7,
+          "marginal_rmse": 0.0009536301832030011,
+          "mean_default_rate": 0.002348684210526316,
+          "n_unique_scenarios": 72,
+          "corr_rmse": 0.3678959557141127,
+          "degenerate": false
+        },
+        "entangled_born_machine": {
+          "shape": [
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+        "Sovereign B",
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+      }
+    }
+  }
+}
\ No newline at end of file
diff --git a/data/scenario_dataset/real_gsib/moments.json b/data/scenario_dataset/real_gsib/moments.json
new file mode 100644
index 0000000..ef61a06
--- /dev/null
+++ b/data/scenario_dataset/real_gsib/moments.json
@@ -0,0 +1,30 @@
+{
+  "bernoulli": {
+    "marginal_rmse": 0.0006052424481582826,
+    "mean_default_rate": 0.0023947368421052633,
+    "n_unique_scenarios": 54,
+    "corr_rmse": 0.46893489166667135,
+    "degenerate": false
+  },
+  "gaussian_copula": {
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+    "mean_default_rate": 0.0022500000000000003,
+    "n_unique_scenarios": 72,
+    "corr_rmse": 0.44637849208606306,
+    "degenerate": false
+  },
+  "student_t_copula": {
+    "marginal_rmse": 0.0009536301832030011,
+    "mean_default_rate": 0.002348684210526316,
+    "n_unique_scenarios": 72,
+    "corr_rmse": 0.3678959557141127,
+    "degenerate": false
+  },
+  "entangled_born_machine": {
+    "marginal_rmse": 0.0006326136261573885,
+    "mean_default_rate": 0.002111842105263158,
+    "n_unique_scenarios": 53,
+    "corr_rmse": 0.43856411668045475,
+    "degenerate": false
+  }
+}
\ No newline at end of file
diff --git a/data/scenario_dataset/real_gsib/real_gsib__bernoulli.csv b/data/scenario_dataset/real_gsib/real_gsib__bernoulli.csv
new file mode 100644
index 0000000..1ad1cd1
--- /dev/null
+++ b/data/scenario_dataset/real_gsib/real_gsib__bernoulli.csv
@@ -0,0 +1,4001 @@
+JPMorgan Chase,Bank of America,Citigroup,Wells Fargo,Goldman Sachs,Morgan Stanley,U.S. Bancorp,PNC Financial,Truist Financial,Bank of New York Mellon,State Street,HSBC Holdings,Barclays,Lloyds Banking Group,Deutsche Bank,Banco Santander,Banco Bilbao Vizcaya Argentaria,ING Groep,UBS Group,Mitsubishi UFJ Financial Group,Sumitomo Mitsui Financial Group,Mizuho Financial Group,Nomura Holdings,Royal Bank of Canada,Toronto-Dominion Bank,Bank of Nova Scotia,Itau Unibanco,Banco Bradesco,Apple,Microsoft,ExxonMobil,Walmart,Boeing,AT&T,Toyota Motor,Volkswagen,Siemens,Petrobras
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diff --git a/data/scenario_dataset/real_gsib/real_gsib__entangled_born_machine.csv b/data/scenario_dataset/real_gsib/real_gsib__entangled_born_machine.csv
new file mode 100644
index 0000000..d465642
--- /dev/null
+++ b/data/scenario_dataset/real_gsib/real_gsib__entangled_born_machine.csv
@@ -0,0 +1,4001 @@
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diff --git a/data/scenario_dataset/real_gsib/real_gsib__gaussian_copula.csv b/data/scenario_dataset/real_gsib/real_gsib__gaussian_copula.csv
new file mode 100644
index 0000000..49665a1
--- /dev/null
+++ b/data/scenario_dataset/real_gsib/real_gsib__gaussian_copula.csv
@@ -0,0 +1,4001 @@
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diff --git a/data/scenario_dataset/real_gsib/real_gsib__student_t_copula.csv b/data/scenario_dataset/real_gsib/real_gsib__student_t_copula.csv
new file mode 100644
index 0000000..c6191b7
--- /dev/null
+++ b/data/scenario_dataset/real_gsib/real_gsib__student_t_copula.csv
@@ -0,0 +1,4001 @@
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diff --git a/data/scenario_dataset/real_gsib/spec.json b/data/scenario_dataset/real_gsib/spec.json
new file mode 100644
index 0000000..8545118
--- /dev/null
+++ b/data/scenario_dataset/real_gsib/spec.json
@@ -0,0 +1,3575 @@
+{
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+    "Barclays",
+    "Lloyds Banking Group",
+    "Deutsche Bank",
+    "Banco Santander",
+    "Banco Bilbao Vizcaya Argentaria",
+    "ING Groep",
+    "UBS Group",
+    "Mitsubishi UFJ Financial Group",
+    "Sumitomo Mitsui Financial Group",
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+    "Nomura Holdings",
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+    "feature_schema": {
+      "fields": [
+        {
+          "name": "node_id",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Stable institution identifier.",
+          "visible_to": [
+            "generator",
+            "simulator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "name",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Human-readable institution name.",
+          "visible_to": [
+            "visualization"
+          ]
+        },
+        {
+          "name": "node_type",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Institution class (bank/insurer/...).",
+          "visible_to": [
+            "generator",
+            "simulator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "business_type",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Business model (universal/investment/regional/custodian).",
+          "visible_to": [
+            "visualization"
+          ]
+        },
+        {
+          "name": "region",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Geographic region.",
+          "visible_to": [
+            "visualization"
+          ]
+        },
+        {
+          "name": "rating",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Public S&P long-term issuer rating.",
+          "visible_to": [
+            "visualization"
+          ]
+        },
+        {
+          "name": "marginal_default_prob",
+          "level": "node",
+          "dtype": "float",
+          "description": "p_i: 1-year marginal default probability from rating.",
+          "visible_to": [
+            "generator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "capital_buffer",
+          "level": "node",
+          "dtype": "float",
+          "description": "Tier-1-style loss-absorbing buffer used by the cascade.",
+          "visible_to": [
+            "simulator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "interbank_assets",
+          "level": "node",
+          "dtype": "float",
+          "description": "Total interbank claims (row-sum constraint for edges).",
+          "visible_to": []
+        },
+        {
+          "name": "interbank_liabilities",
+          "level": "node",
+          "dtype": "float",
+          "description": "Total interbank liabilities (col-sum constraint for edges).",
+          "visible_to": []
+        },
+        {
+          "name": "correlation_matrix",
+          "level": "system",
+          "dtype": "matrix",
+          "description": "Equity-return latent correlation (copula dependency target).",
+          "visible_to": [
+            "generator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "exposure_matrix",
+          "level": "edge",
+          "dtype": "matrix",
+          "description": "W[i,j] = loss to i if j defaults (reconstructed bilateral).",
+          "visible_to": [
+            "simulator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "cluster",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Community label from detection on the network.",
+          "visible_to": [
+            "visualization"
+          ]
+        }
+      ]
+    },
+    "provenance_notes": "Bilateral exposures are reconstructed (real bilateral data is confidential) and risk-adjusted into an effective-loss matrix (LGD/maturity/wrong-way/substitutability); marginals and equity correlation are empirical. Corporates borrow from banks but do not lend interbank (directed bank->corporate edges)."
+  }
+}
\ No newline at end of file
diff --git a/data/scenario_dataset/synthetic_16/moments.json b/data/scenario_dataset/synthetic_16/moments.json
new file mode 100644
index 0000000..e11c77c
--- /dev/null
+++ b/data/scenario_dataset/synthetic_16/moments.json
@@ -0,0 +1,30 @@
+{
+  "bernoulli": {
+    "marginal_rmse": 0.002368869422939838,
+    "mean_default_rate": 0.02540625,
+    "n_unique_scenarios": 122,
+    "corr_rmse": 0.19247607671412983,
+    "degenerate": false
+  },
+  "gaussian_copula": {
+    "marginal_rmse": 0.0030662549019893222,
+    "mean_default_rate": 0.024781249999999998,
+    "n_unique_scenarios": 176,
+    "corr_rmse": 0.0626639935705719,
+    "degenerate": false
+  },
+  "student_t_copula": {
+    "marginal_rmse": 0.002111160411327338,
+    "mean_default_rate": 0.024484375,
+    "n_unique_scenarios": 195,
+    "corr_rmse": 0.04798423846096638,
+    "degenerate": false
+  },
+  "entangled_born_machine": {
+    "marginal_rmse": 0.02178975788255229,
+    "mean_default_rate": 0.033828125,
+    "n_unique_scenarios": 271,
+    "corr_rmse": 0.11819369361000172,
+    "degenerate": false
+  }
+}
\ No newline at end of file
diff --git a/data/scenario_dataset/synthetic_16/spec.json b/data/scenario_dataset/synthetic_16/spec.json
new file mode 100644
index 0000000..6be1b2c
--- /dev/null
+++ b/data/scenario_dataset/synthetic_16/spec.json
@@ -0,0 +1,679 @@
+{
+  "node_names": [
+    "Global Bank A",
+    "Global Bank B",
+    "Regional Bank",
+    "Mortgage Lender",
+    "Life Insurer",
+    "Reinsurer",
+    "Pension Fund",
+    "Credit Fund",
+    "Real Estate Fund",
+    "Industrial Corporate",
+    "Energy Corporate",
+    "Retail Corporate",
+    "Sovereign A",
+    "Sovereign B",
+    "Central Counterparty",
+    "Payments Utility"
+  ],
+  "node_types": [
+    "bank",
+    "bank",
+    "bank",
+    "bank",
+    "insurer",
+    "insurer",
+    "fund",
+    "fund",
+    "fund",
+    "corporate",
+    "corporate",
+    "corporate",
+    "sovereign",
+    "sovereign",
+    "CCP",
+    "CCP"
+  ],
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+      0.13818127702729494,
+      0.14733470338169746,
+      0.14416796642856666,
+      0.1,
+      0.56,
+      0.38,
+      0.479328332126728,
+      1.0
+    ]
+  ],
+  "target_joint_probs": null,
+  "clusters": [
+    "banks",
+    "banks",
+    "banks",
+    "banks",
+    "insurers",
+    "insurers",
+    "funds",
+    "funds",
+    "funds",
+    "corporates",
+    "corporates",
+    "corporates",
+    "sovereign_ccp",
+    "sovereign_ccp",
+    "sovereign_ccp",
+    "sovereign_ccp"
+  ],
+  "metadata": {
+    "name": "Synthetic systemic stress network",
+    "seed": 7,
+    "correlation_space": "binary_default",
+    "description": "Directed weighted exposures with visible financial communities."
+  }
+}
\ No newline at end of file
diff --git a/data/scenario_dataset/synthetic_16/synthetic_16__bernoulli.csv b/data/scenario_dataset/synthetic_16/synthetic_16__bernoulli.csv
new file mode 100644
index 0000000..3eb6ea1
--- /dev/null
+++ b/data/scenario_dataset/synthetic_16/synthetic_16__bernoulli.csv
@@ -0,0 +1,4001 @@
+Global Bank A,Global Bank B,Regional Bank,Mortgage Lender,Life Insurer,Reinsurer,Pension Fund,Credit Fund,Real Estate Fund,Industrial Corporate,Energy Corporate,Retail Corporate,Sovereign A,Sovereign B,Central Counterparty,Payments Utility
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diff --git a/data/scenario_dataset/synthetic_16/synthetic_16__entangled_born_machine.csv b/data/scenario_dataset/synthetic_16/synthetic_16__entangled_born_machine.csv
new file mode 100644
index 0000000..ef92082
--- /dev/null
+++ b/data/scenario_dataset/synthetic_16/synthetic_16__entangled_born_machine.csv
@@ -0,0 +1,4001 @@
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diff --git a/data/scenario_dataset/synthetic_16/synthetic_16__gaussian_copula.csv b/data/scenario_dataset/synthetic_16/synthetic_16__gaussian_copula.csv
new file mode 100644
index 0000000..65eecd3
--- /dev/null
+++ b/data/scenario_dataset/synthetic_16/synthetic_16__gaussian_copula.csv
@@ -0,0 +1,4001 @@
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diff --git a/data/scenario_dataset/synthetic_54/moments.json b/data/scenario_dataset/synthetic_54/moments.json
new file mode 100644
index 0000000..85dd1ef
--- /dev/null
+++ b/data/scenario_dataset/synthetic_54/moments.json
@@ -0,0 +1,30 @@
+{
+  "bernoulli": {
+    "marginal_rmse": 0.0013196169477878378,
+    "mean_default_rate": 0.011999999999999999,
+    "n_unique_scenarios": 313,
+    "corr_rmse": 0.020914191448296073,
+    "degenerate": false
+  },
+  "gaussian_copula": {
+    "marginal_rmse": 0.0018608291660400175,
+    "mean_default_rate": 0.012407407407407407,
+    "n_unique_scenarios": 367,
+    "corr_rmse": 0.03176753134435303,
+    "degenerate": false
+  },
+  "student_t_copula": {
+    "marginal_rmse": 0.001813708090359923,
+    "mean_default_rate": 0.01261111111111111,
+    "n_unique_scenarios": 400,
+    "corr_rmse": 0.1005041548982943,
+    "degenerate": false
+  },
+  "entangled_born_machine": {
+    "marginal_rmse": 0.0015610863975900695,
+    "mean_default_rate": 0.011819444444444445,
+    "n_unique_scenarios": 317,
+    "corr_rmse": 0.01611964525281019,
+    "degenerate": false
+  }
+}
\ No newline at end of file
diff --git a/data/scenario_dataset/synthetic_54/spec.json b/data/scenario_dataset/synthetic_54/spec.json
new file mode 100644
index 0000000..c898ab4
--- /dev/null
+++ b/data/scenario_dataset/synthetic_54/spec.json
@@ -0,0 +1,9466 @@
+{
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+    ],
+    "edge_model": {
+      "channels": [
+        "lgd_recovery",
+        "maturity_rollover",
+        "wrong_way",
+        "substitutability"
+      ],
+      "directed": true,
+      "rollover_beta": 0.6,
+      "rollover_tau": 1.0,
+      "wrong_way_gamma": 0.5,
+      "concentration_delta": 0.5,
+      "mean_recovery": 0.4724,
+      "mean_maturity_stress": 1.2708,
+      "effective_to_notional_ratio": 0.7164
+    },
+    "type_counts": {
+      "CCP": 3,
+      "bank": 16,
+      "corporate": 12,
+      "fund": 12,
+      "insurer": 7,
+      "sovereign": 4
+    },
+    "pd_source": "Moody's Corporate Default & Recovery Rates 1920-2004, Exhibit 17 (whole-letter, Year-1)",
+    "calibration_sources": [
+      "Moody's Corporate Default & Recovery Rates 1920-2004, Exhibit 17 (whole-letter, Year-1)",
+      "Cimini et al. 2015 (density-corrected gravity reconstruction)",
+      "Bardoscia et al. 2021 (scale-free core-periphery interbank topology)",
+      "Gai-Kapadia 2010 / Basel III (capital buffers, single-counterparty cap)"
+    ],
+    "description": "Programmatic institution mix on a sparse scale-free core-periphery exposure graph; per-rating marginals, 4-8% Tier-1 buffers, <=25%-of-Tier-1 single-counterparty cap, ~20% interbank-asset share."
+  }
+}
\ No newline at end of file
diff --git a/data/scenario_dataset/synthetic_54/synthetic_54__bernoulli.csv b/data/scenario_dataset/synthetic_54/synthetic_54__bernoulli.csv
new file mode 100644
index 0000000..7797266
--- /dev/null
+++ b/data/scenario_dataset/synthetic_54/synthetic_54__bernoulli.csv
@@ -0,0 +1,4001 @@
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diff --git a/data/scenario_dataset/synthetic_54/synthetic_54__entangled_born_machine.csv b/data/scenario_dataset/synthetic_54/synthetic_54__entangled_born_machine.csv
new file mode 100644
index 0000000..3c075b8
--- /dev/null
+++ b/data/scenario_dataset/synthetic_54/synthetic_54__entangled_born_machine.csv
@@ -0,0 +1,4001 @@
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diff --git a/data/scenario_dataset/synthetic_54/synthetic_54__gaussian_copula.csv b/data/scenario_dataset/synthetic_54/synthetic_54__gaussian_copula.csv
new file mode 100644
index 0000000..1e2195b
--- /dev/null
+++ b/data/scenario_dataset/synthetic_54/synthetic_54__gaussian_copula.csv
@@ -0,0 +1,4001 @@
+Global Bank A,Global Bank B,Continental Bank,Regional Bank,Community Bank,Mortgage Lender,Trade Finance Bank,Investment Bank,Merchant Bank,Universal Bank,Savings Bank,Commercial Bank,Private Bank,Custody Bank,Cooperative Bank,Digital Bank,Pension Fund,Credit Fund,Real Estate Fund,Money Market Fund,Hedge Fund,Sovereign Wealth Fund,Mutual Fund,Asset Manager,Private Equity Fund,Infrastructure Fund,Bond Fund,Equity Fund,Industrial Corporate,Energy Corporate,Retail Corporate,Transport Corporate,Telecom Corporate,Utility Corporate,Healthcare Corporate,Materials Corporate,Consumer Goods Corporate,Technology Corporate,Construction Corporate,Aerospace Corporate,Life Insurer,Reinsurer,Health Insurer,Property Insurer,Casualty Insurer,Credit Insurer,Title Insurer,Sovereign A,Sovereign B,Sovereign C,Sovereign D,Central Counterparty,Payments Utility,Securities Depository
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diff --git a/data/scenario_dataset/synthetic_54/synthetic_54__student_t_copula.csv b/data/scenario_dataset/synthetic_54/synthetic_54__student_t_copula.csv
new file mode 100644
index 0000000..0bd3899
--- /dev/null
+++ b/data/scenario_dataset/synthetic_54/synthetic_54__student_t_copula.csv
@@ -0,0 +1,4001 @@
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literal 0
HcmV?d00001

diff --git a/scripts/build_dataset.py b/scripts/build_dataset.py
index f19f968..eca3a80 100644
--- a/scripts/build_dataset.py
+++ b/scripts/build_dataset.py
@@ -5,7 +5,7 @@
 them side-by-side, so the files can be used to train/benchmark generators or to
 compare classical vs quantum samplers on a common footing.
 
-Layout (under --out-dir, default outputs/dataset/):
+Layout (under --out-dir, default data/scenario_dataset/):
 
     <source>/
         spec.json                      # the SystemSpec (marginals, correlation, ...)
@@ -94,7 +94,7 @@ def main() -> None:
     ap.add_argument("--seed", type=int, default=12345)
     ap.add_argument("--sources", nargs="*", default=list(SOURCES), choices=list(SOURCES))
     ap.add_argument("--generators", nargs="*", default=list(GENERATORS), choices=list(GENERATORS))
-    ap.add_argument("--out-dir", type=Path, default=Path("outputs/dataset"))
+    ap.add_argument("--out-dir", type=Path, default=Path("data/scenario_dataset"))
     args = ap.parse_args()
 
     args.out_dir.mkdir(parents=True, exist_ok=True)

From d2b82ed06df8c29f121231904aae1bf9bef5bd96 Mon Sep 17 00:00:00 2001
From: Alex Do <alexxdo2004@gmail.com>
Date: Sun, 7 Jun 2026 04:18:47 +0300
Subject: [PATCH 5/5] feat: results and shit

---
 .gitignore                                    |      5 +-
 .../analysis_stress_comparison.json           |    338 +
 .../analysis_stress_comparison.png            |    Bin 0 -> 138952 bytes
 .../analysis_stress_comparison.txt            |     86 +
 ...uster0_ibm_boston_d8ibcqpe8nrc73bi4ha0.npz |    Bin 0 -> 705412 bytes
 ...uster1_ibm_boston_d8ibddc2upec739m0t80.npz |    Bin 0 -> 732105 bytes
 ...uster2_ibm_boston_d8ibe3k2upec739m0tu0.npz |    Bin 0 -> 418218 bytes
 .../entity_scenario_vs_hardware.csv           |     39 +
 .../entity_scenario_vs_hardware.json          |    778 +
 .../entity_scenario_vs_hardware.png           |    Bin 0 -> 247386 bytes
 .../entity_scenario_vs_hardware.txt           |     45 +
 .../job_ids_live.json                         |     38 +
 .../joint_counts.csv                          | 140072 +++++++++++++++
 .../joint_counts_hist.png                     |    Bin 0 -> 96991 bytes
 .../real_cluster_mixture_stress_hw/plan.json  |    233 +
 .../qubit_legend.csv                          |     39 +
 .../qubit_legend.json                         |    382 +
 .../real_stress_hardware_run_report.json      |    500 +
 .../reconciled_global_stress.npz              |    Bin 0 -> 5026094 bytes
 .../stressed_spec.json                        |   3594 +
 .../analyze_entity_scenario_vs_hardware.py    |    423 +
 .../analyze_real_cluster_mixture_stress_hw.py |    551 +
 scripts/export_joint_counts.py                |    120 +
 scripts/run_real_cluster_mixture_stress.py    |    267 +
 ...un_real_cluster_mixture_stress_hardware.py |    404 +
 src/systemic_risk/data_network/__init__.py    |     11 +
 src/systemic_risk/data_network/stress.py      |    218 +
 tests/test_stress.py                          |     90 +
 28 files changed, 148230 insertions(+), 3 deletions(-)
 create mode 100644 outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.json
 create mode 100644 outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.png
 create mode 100644 outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.txt
 create mode 100644 outputs/real_cluster_mixture_stress_hw/cluster0_ibm_boston_d8ibcqpe8nrc73bi4ha0.npz
 create mode 100644 outputs/real_cluster_mixture_stress_hw/cluster1_ibm_boston_d8ibddc2upec739m0t80.npz
 create mode 100644 outputs/real_cluster_mixture_stress_hw/cluster2_ibm_boston_d8ibe3k2upec739m0tu0.npz
 create mode 100644 outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.csv
 create mode 100644 outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.json
 create mode 100644 outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.png
 create mode 100644 outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.txt
 create mode 100644 outputs/real_cluster_mixture_stress_hw/job_ids_live.json
 create mode 100644 outputs/real_cluster_mixture_stress_hw/joint_counts.csv
 create mode 100644 outputs/real_cluster_mixture_stress_hw/joint_counts_hist.png
 create mode 100644 outputs/real_cluster_mixture_stress_hw/plan.json
 create mode 100644 outputs/real_cluster_mixture_stress_hw/qubit_legend.csv
 create mode 100644 outputs/real_cluster_mixture_stress_hw/qubit_legend.json
 create mode 100644 outputs/real_cluster_mixture_stress_hw/real_stress_hardware_run_report.json
 create mode 100644 outputs/real_cluster_mixture_stress_hw/reconciled_global_stress.npz
 create mode 100644 outputs/real_cluster_mixture_stress_hw/stressed_spec.json
 create mode 100644 scripts/analyze_entity_scenario_vs_hardware.py
 create mode 100644 scripts/analyze_real_cluster_mixture_stress_hw.py
 create mode 100644 scripts/export_joint_counts.py
 create mode 100644 scripts/run_real_cluster_mixture_stress.py
 create mode 100644 scripts/run_real_cluster_mixture_stress_hardware.py
 create mode 100644 src/systemic_risk/data_network/stress.py
 create mode 100644 tests/test_stress.py

diff --git a/.gitignore b/.gitignore
index 275e6df..d0692dd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,9 +6,8 @@ __pycache__/
 .mypy_cache/
 .ruff_cache/
 
-outputs/
-outputs/scenarios/
-outputs/scenarios/*.csv
+outputs/*
+!outputs/real_cluster_mixture_stress_hw/
 *.egg-info/
 .DS_Store
 .env
diff --git a/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.json b/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.json
new file mode 100644
index 0000000..711f83a
--- /dev/null
+++ b/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.json
@@ -0,0 +1,338 @@
+{
+  "title": "Stress-regime hardware comparative analysis (REAL 38-entity network)",
+  "backend": "ibm_boston",
+  "partition": {
+    "cluster_sizes": [
+      14,
+      14,
+      10
+    ],
+    "n": 38
+  },
+  "noise_floor": 0.027,
+  "regimes": {
+    "baseline_hw": "real PDs (~0.2% mean), all 38 below 2.7% floor",
+    "stress_hw": "2008-calibrated PDs (~15% mean), all 38 above floor",
+    "sim_preview": "exact statevector on the stressed spec (faithful target)"
+  },
+  "1_stress_hw_fidelity": {
+    "per_cluster": [
+      {
+        "run": "stress_hw",
+        "cluster": 0,
+        "qubits": 14,
+        "transpiled_depth": 751,
+        "transpiled_2q_gates": 478,
+        "target_marg_mean": 0.10697,
+        "target_marg_min": 0.05687,
+        "target_marg_max": 0.23799,
+        "all_target_below_noise_floor": false,
+        "n_target_above_floor": 14,
+        "obs_marg_mean": 0.31656,
+        "marginal_rmse_vs_target": 0.23834,
+        "marginal_tv_vs_target": 0.20958,
+        "marginal_spearman_vs_target": -0.4232,
+        "target_within_abs_corr": 0.3647,
+        "obs_within_abs_corr": 0.1368,
+        "within_corr_recovery_fraction": 0.3751,
+        "marginal_mix_pull": 0.31656,
+        "marginal_signed_bias": 0.20958
+      },
+      {
+        "run": "stress_hw",
+        "cluster": 1,
+        "qubits": 14,
+        "transpiled_depth": 702,
+        "transpiled_2q_gates": 459,
+        "target_marg_mean": 0.23079,
+        "target_marg_min": 0.07443,
+        "target_marg_max": 0.58605,
+        "all_target_below_noise_floor": false,
+        "n_target_above_floor": 14,
+        "obs_marg_mean": 0.33392,
+        "marginal_rmse_vs_target": 0.14276,
+        "marginal_tv_vs_target": 0.1108,
+        "marginal_spearman_vs_target": 0.841,
+        "target_within_abs_corr": 0.2526,
+        "obs_within_abs_corr": 0.1082,
+        "within_corr_recovery_fraction": 0.4283,
+        "marginal_mix_pull": 0.27611,
+        "marginal_signed_bias": 0.10312
+      },
+      {
+        "run": "stress_hw",
+        "cluster": 2,
+        "qubits": 10,
+        "transpiled_depth": 455,
+        "transpiled_2q_gates": 211,
+        "target_marg_mean": 0.09713,
+        "target_marg_min": 0.027,
+        "target_marg_max": 0.23799,
+        "all_target_below_noise_floor": false,
+        "n_target_above_floor": 10,
+        "obs_marg_mean": 0.23065,
+        "marginal_rmse_vs_target": 0.15975,
+        "marginal_tv_vs_target": 0.13352,
+        "marginal_spearman_vs_target": 0.3941,
+        "target_within_abs_corr": 0.1363,
+        "obs_within_abs_corr": 0.1794,
+        "within_corr_recovery_fraction": 1.3162,
+        "marginal_mix_pull": 0.23065,
+        "marginal_signed_bias": 0.13352
+      }
+    ],
+    "overall": {
+      "marginal_rmse_vs_target": 0.18749,
+      "marginal_tv_vs_target": 0.15317,
+      "marginal_spearman_vs_target_global": 0.3618,
+      "mean_signed_bias": 0.15035,
+      "target_mean": 0.15,
+      "obs_mean": 0.30035,
+      "mean_within_corr_recovery_fraction": 0.7065,
+      "cross_cluster_corr_reconciled": 0.17096,
+      "cross_cluster_corr_reference": 0.17711,
+      "cross_cluster_corr_recovery_fraction": 0.9653,
+      "count_tv_hw_vs_simpreview": 0.25643,
+      "count_tv_hw_vs_reference": 0.43558
+    }
+  },
+  "2_before_after_baseline_vs_stress": {
+    "baseline_per_cluster": [
+      {
+        "run": "baseline_hw",
+        "cluster": 0,
+        "qubits": 14,
+        "transpiled_depth": 893,
+        "transpiled_2q_gates": 488,
+        "target_marg_mean": 0.00126,
+        "target_marg_min": 0.0006,
+        "target_marg_max": 0.0031,
+        "all_target_below_noise_floor": true,
+        "n_target_above_floor": 0,
+        "obs_marg_mean": 0.20107,
+        "marginal_rmse_vs_target": 0.22511,
+        "marginal_tv_vs_target": 0.1998,
+        "marginal_spearman_vs_target": 0.16,
+        "target_within_abs_corr": 0.1492,
+        "obs_within_abs_corr": 0.0555,
+        "within_corr_recovery_fraction": 0.372,
+        "marginal_mix_pull": 0.20107,
+        "marginal_signed_bias": 0.1998
+      },
+      {
+        "run": "baseline_hw",
+        "cluster": 1,
+        "qubits": 14,
+        "transpiled_depth": 601,
+        "transpiled_2q_gates": 383,
+        "target_marg_mean": 0.00426,
+        "target_marg_min": 0.0008,
+        "target_marg_max": 0.0139,
+        "all_target_below_noise_floor": true,
+        "n_target_above_floor": 0,
+        "obs_marg_mean": 0.15701,
+        "marginal_rmse_vs_target": 0.19599,
+        "marginal_tv_vs_target": 0.15274,
+        "marginal_spearman_vs_target": 0.2413,
+        "target_within_abs_corr": 0.0838,
+        "obs_within_abs_corr": 0.0718,
+        "within_corr_recovery_fraction": 0.8568,
+        "marginal_mix_pull": 0.14934,
+        "marginal_signed_bias": 0.15274
+      },
+      {
+        "run": "baseline_hw",
+        "cluster": 2,
+        "qubits": 10,
+        "transpiled_depth": 105,
+        "transpiled_2q_gates": 38,
+        "target_marg_mean": 0.00112,
+        "target_marg_min": 1e-05,
+        "target_marg_max": 0.0031,
+        "all_target_below_noise_floor": true,
+        "n_target_above_floor": 0,
+        "obs_marg_mean": 0.01635,
+        "marginal_rmse_vs_target": 0.01956,
+        "marginal_tv_vs_target": 0.01523,
+        "marginal_spearman_vs_target": 0.4195,
+        "target_within_abs_corr": 0.0357,
+        "obs_within_abs_corr": 0.0663,
+        "within_corr_recovery_fraction": 1.8571,
+        "marginal_mix_pull": 0.01635,
+        "marginal_signed_bias": 0.01523
+      }
+    ],
+    "stress_per_cluster": [
+      {
+        "run": "stress_hw",
+        "cluster": 0,
+        "qubits": 14,
+        "transpiled_depth": 751,
+        "transpiled_2q_gates": 478,
+        "target_marg_mean": 0.10697,
+        "target_marg_min": 0.05687,
+        "target_marg_max": 0.23799,
+        "all_target_below_noise_floor": false,
+        "n_target_above_floor": 14,
+        "obs_marg_mean": 0.31656,
+        "marginal_rmse_vs_target": 0.23834,
+        "marginal_tv_vs_target": 0.20958,
+        "marginal_spearman_vs_target": -0.4232,
+        "target_within_abs_corr": 0.3647,
+        "obs_within_abs_corr": 0.1368,
+        "within_corr_recovery_fraction": 0.3751,
+        "marginal_mix_pull": 0.31656,
+        "marginal_signed_bias": 0.20958
+      },
+      {
+        "run": "stress_hw",
+        "cluster": 1,
+        "qubits": 14,
+        "transpiled_depth": 702,
+        "transpiled_2q_gates": 459,
+        "target_marg_mean": 0.23079,
+        "target_marg_min": 0.07443,
+        "target_marg_max": 0.58605,
+        "all_target_below_noise_floor": false,
+        "n_target_above_floor": 14,
+        "obs_marg_mean": 0.33392,
+        "marginal_rmse_vs_target": 0.14276,
+        "marginal_tv_vs_target": 0.1108,
+        "marginal_spearman_vs_target": 0.841,
+        "target_within_abs_corr": 0.2526,
+        "obs_within_abs_corr": 0.1082,
+        "within_corr_recovery_fraction": 0.4283,
+        "marginal_mix_pull": 0.27611,
+        "marginal_signed_bias": 0.10312
+      },
+      {
+        "run": "stress_hw",
+        "cluster": 2,
+        "qubits": 10,
+        "transpiled_depth": 455,
+        "transpiled_2q_gates": 211,
+        "target_marg_mean": 0.09713,
+        "target_marg_min": 0.027,
+        "target_marg_max": 0.23799,
+        "all_target_below_noise_floor": false,
+        "n_target_above_floor": 10,
+        "obs_marg_mean": 0.23065,
+        "marginal_rmse_vs_target": 0.15975,
+        "marginal_tv_vs_target": 0.13352,
+        "marginal_spearman_vs_target": 0.3941,
+        "target_within_abs_corr": 0.1363,
+        "obs_within_abs_corr": 0.1794,
+        "within_corr_recovery_fraction": 1.3162,
+        "marginal_mix_pull": 0.23065,
+        "marginal_signed_bias": 0.13352
+      }
+    ],
+    "summary": {
+      "marginal_rmse_vs_target": {
+        "baseline_hw": 0.18145,
+        "stress_hw": 0.18749
+      },
+      "marginal_spearman_vs_target_global": {
+        "baseline_hw": 0.2485,
+        "stress_hw": 0.3618
+      },
+      "marginal_spearman_per_cluster": {
+        "baseline_hw": [
+          0.16,
+          0.2413,
+          0.4195
+        ],
+        "stress_hw": [
+          -0.4232,
+          0.841,
+          0.3941
+        ]
+      },
+      "mean_within_corr_recovery_fraction": {
+        "baseline_hw": 1.0286,
+        "stress_hw": 0.7065
+      },
+      "within_corr_recovery_per_cluster": {
+        "baseline_hw": [
+          0.372,
+          0.8568,
+          1.8571
+        ],
+        "stress_hw": [
+          0.3751,
+          0.4283,
+          1.3162
+        ]
+      },
+      "cross_cluster_corr_recovery_fraction": {
+        "baseline_hw": 0.9452,
+        "stress_hw": 0.9653
+      }
+    },
+    "cascade_tail_risk": {
+      "baseline_hw": {
+        "p_severe_reference": NaN,
+        "p_severe_reconciled": NaN,
+        "p_severe_independent": NaN,
+        "cvar_reference": 1.7358,
+        "cvar_reconciled": 29.3765,
+        "cvar_independent": 24.5767,
+        "mean_cascade_reference": 0.1067,
+        "mean_cascade_reconciled": 6.2207,
+        "mean_cascade_independent": 5.9428,
+        "count_tv_reconciled_vs_ref": 0.92289
+      },
+      "stress_hw": {
+        "p_severe_reference": 0.1455,
+        "p_severe_reconciled": 0.33825,
+        "p_severe_independent": 0.28575,
+        "cvar_reference": 33.0103,
+        "cvar_reconciled": 35.4395,
+        "cvar_independent": 34.5,
+        "mean_cascade_reference": 7.9893,
+        "mean_cascade_reconciled": 16.4155,
+        "mean_cascade_independent": 16.1028,
+        "count_tv_reconciled_vs_ref": 0.43558
+      },
+      "sim_preview": {
+        "p_severe_reference": 0.1455,
+        "p_severe_reconciled": 0.2135,
+        "p_severe_independent": 0.1475,
+        "cvar_reference": 33.0103,
+        "cvar_reconciled": 35.5094,
+        "cvar_independent": 34.0411,
+        "mean_cascade_reference": 7.9893,
+        "mean_cascade_reconciled": 12.3123,
+        "mean_cascade_independent": 11.5143,
+        "count_tv_reconciled_vs_ref": 0.28052
+      }
+    }
+  },
+  "3_ground_truth_framing": {
+    "note": "reference = full-network Gaussian-copula joint on the STRESSED target; sim_preview = exact statevector (faithful); stress_hw = ibm_boston.",
+    "p_severe": {
+      "reference": 0.1455,
+      "sim_preview": 0.2135,
+      "stress_hw": 0.33825
+    },
+    "cascade_cvar95": {
+      "reference": 33.0103,
+      "sim_preview": 35.5094,
+      "stress_hw": 35.4395
+    },
+    "mean_cascade_count": {
+      "reference": 7.9893,
+      "sim_preview": 12.3123,
+      "stress_hw": 16.4155
+    },
+    "marginal_rmse_vs_ref": {
+      "sim_preview": 0.13564,
+      "stress_hw": 0.18724
+    },
+    "count_tv_vs_ref": {
+      "sim_preview": 0.28052,
+      "stress_hw": 0.43558
+    },
+    "stress_hw_vs_simpreview_count_tv": 0.25643
+  }
+}
\ No newline at end of file
diff --git a/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.png b/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.png
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diff --git a/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.txt b/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.txt
new file mode 100644
index 0000000..7c7f625
--- /dev/null
+++ b/outputs/real_cluster_mixture_stress_hw/analysis_stress_comparison.txt
@@ -0,0 +1,86 @@
+REAL 38-ENTITY HARDWARE -- STRESS vs BASELINE COMPARATIVE ANALYSIS
+====================================================================
+Backend: ibm_boston | 200,000 shots/cluster | same 14/14/10 partition
+BASELINE-HW: real ~0.2% PDs, all 38 below the 2.7% noise floor.
+STRESS-HW:   2008-calibrated ~15% PDs, all 38 ABOVE the floor (SNR mean ~5.6).
+SIM-PREVIEW: exact statevector on the stressed spec = the faithful target.
+Artifacts: analysis_stress_comparison.json (all numbers), .png, this file.
+
+BOTTOM LINE
+-----------
+Lifting the signal above the noise floor turned the real network from UNMEASURABLE into a
+PARTIAL, structurally-meaningful hardware demonstration -- but NOT a magnitude-faithful one.
+What stress BOUGHT us: the marginal ORDERING moves from noise to a genuinely positive rank signal
+(global Spearman vs target +0.25 baseline -> +0.36 stress), and the within-cluster |corr| recovery becomes
+REAL rather than spurious (71% of target on above-floor targets vs the baseline's noise-manufactured
+correlations on ~0 targets). The cross-cluster coupling is reconstructed (97% of reference) -- but that is the
+classical reconciler, not the device.
+What it did NOT buy: faithful marginal MAGNITUDES. Decoherence still pulls every qubit toward
+0.5, so the overall marginal RMSE vs target stays large (0.187, mean signed
+bias +0.150 = upward pull). The cascade tail is therefore HARDWARE-PERTURBED
+(overstated), not ground truth.
+
+1. STRESS-HW ERROR/FIDELITY vs LOADED TARGET (decoherence vs depth)
+------------------------------------------------------------------
+cl qub 2q-gates depth | tgt_marg obs_marg  RMSE | Spear | tgt|c| obs|c| recov
+   0      14     478    751 |  0.1070   0.3166   0.238 |  -0.42 |  0.365  0.137    38%
+   1      14     459    702 |  0.2308   0.3339   0.143 |   0.84 |  0.253  0.108    43%
+   2      10     211    455 |  0.0971   0.2306   0.160 |   0.39 |  0.136  0.179   132%
+overall: marginal RMSE 0.187, TV 0.153, global Spearman +0.36, signed bias +0.150
+         within-corr recovery 71%, cross-cluster corr recovery 97% (0.171 vs ref 0.177)
+         default-count TV: HW vs sim-preview 0.256, HW vs reference 0.436
+- Decoherence bias: mix-pull (0=pure,0.5=mixed) tracks depth -- c0=0.32@d751, c1=0.28@d702, c2=0.23@d455. The deepest 14q blocks (478/459 2q-gates) inflate most; the 10q block (211 gates) leaks least.
+- Even above the floor, marginals are biased UP toward 0.5 -- the magnitudes are NOT trustworthy; the ORDER is.
+
+2. BEFORE/AFTER -- BASELINE-HW vs STRESS-HW (what above-floor signal bought)
+------------------------------------------------------------------
+metric                                   baseline-HW    stress-HW
+marginal RMSE vs target                      0.181        0.187
+marginal Spearman vs target (GLOBAL)         +0.25        +0.36
+  per-cluster Spearman [c0,c1,c2]   base [0.16, 0.2413, 0.4195]  stress [-0.4232, 0.841, 0.3941]
+within-cluster |corr| recovery (mean)        103%          71%
+cross-cluster corr recovery                   95%          97%
+
+   Cascade tail risk (post-contagion count), reconciled global joint:
+   (P(severe) is omitted here: baseline & stress use DIFFERENT references, so it
+    is not comparable across the two runs -- see section 3 for stress-vs-truth.)
+   metric                  baseline-HW   stress-HW   sim-preview
+   CVaR95 cascade count        29.38      35.44      35.51
+   mean cascade count           6.22      16.42      12.31
+   count-TV vs (own) ref       0.923      0.436      0.281
+- Marginal ordering: baseline-HW global Spearman is a weak +0.25 dominated by noise (targets all
+  buried under the floor; per-cluster it is [0.16, 0.24, 0.42]); stress-HW rises to +0.36 GLOBALLY and to
+  +0.84 on cluster 1 -- the device now sees WHICH entities are riskier where the signal is strongest.
+  NOTE: cluster 0 actually regresses (Spearman -0.42): recovery is real but UNEVEN, not uniform.
+- Correlations: the baseline's 103% within-|corr| 'recovery' is SPURIOUS -- noise manufacturing
+  correlation on ~0 targets (per-cluster up to 186%). Stress-HW's 71% is a GENUINE retention of real,
+  above-floor entanglement structure. So the honest story is spurious->real, not low->high.
+- Cascade: baseline-HW's reconciled count law is TV 0.92 from its own reference (no signal, ~ the
+  independent baseline); stress-HW closes that to TV 0.44 -- markedly closer to the right distribution,
+  though still OVERSTATED by the marginal inflation (see section 3).
+
+3. GROUND-TRUTH FRAMING -- stress-HW vs sim-preview vs reference (tail risk)
+------------------------------------------------------------------
+metric                  reference   sim-preview(faithful)   stress-HW
+P(severe)                  0.145           0.213           0.338
+CVaR95 cascade count       33.01           35.51           35.44
+mean cascade count          7.99           12.31           16.42
+marginal RMSE vs ref          --             0.136           0.187
+count-TV vs ref               --             0.281           0.436
+- Hardware lands ABOVE the faithful sim-preview on every tail metric: it overstates P(severe)
+  (0.34 vs sim 0.21, ref 0.15) and mean cascade (16.4 vs sim 12.3). CVaR is
+  closer (saturates near the system size). The HW reconciled count-distribution sits TV 0.26 from
+  the faithful sim-preview -- same regime, hardware-perturbed.
+
+VERDICT
+-------
+YES -- the stress regime makes the real 38-entity network a LEGITIMATE, if qualified, hardware
+demonstration. Above the noise floor the QPU recovers a real (if uneven) marginal ORDERING and
+GENUINELY retains within-cluster correlation (71% of target) instead of the baseline's noise-manufactured
+spurious correlation -- and the cascade count law moves from TV 0.92 (no signal) to TV 0.44 vs truth.
+This is a real signal, not decoherence -- the before/after vs the buried baseline is unambiguous.
+CAVEATS: (1) marginal MAGNITUDES remain decoherence-biased upward toward 0.5 (RMSE ~0.19),
+so absolute PDs are not trustworthy; (2) the cascade tail numbers are therefore HARDWARE-PERTURBED and
+overstate risk relative to the faithful sim-preview; (3) the cross-cluster correlation is rebuilt by
+the CLASSICAL reconciler (fit to the spec target), not measured on the device. Honest framing:
+a correlation/ordering-faithful hardware demo, magnitude- and tail-perturbed.
diff --git a/outputs/real_cluster_mixture_stress_hw/cluster0_ibm_boston_d8ibcqpe8nrc73bi4ha0.npz b/outputs/real_cluster_mixture_stress_hw/cluster0_ibm_boston_d8ibcqpe8nrc73bi4ha0.npz
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diff --git a/outputs/real_cluster_mixture_stress_hw/cluster1_ibm_boston_d8ibddc2upec739m0t80.npz b/outputs/real_cluster_mixture_stress_hw/cluster1_ibm_boston_d8ibddc2upec739m0t80.npz
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diff --git a/outputs/real_cluster_mixture_stress_hw/cluster2_ibm_boston_d8ibe3k2upec739m0tu0.npz b/outputs/real_cluster_mixture_stress_hw/cluster2_ibm_boston_d8ibe3k2upec739m0tu0.npz
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diff --git a/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.csv b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.csv
new file mode 100644
index 0000000..d3abfc3
--- /dev/null
+++ b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.csv
@@ -0,0 +1,39 @@
+qubit,ticker,name,node_type,business_type,region,cluster,bloc,baseline_target_pd,ber_pd,gcop_pd,tcop_pd,ebm_pd,hw_stressed_target_pd,hw_observed_pd,ebm_mean_abs_corr,hw_mean_abs_corr
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+6,USB,U.S. Bancorp,bank,regional,US,0,A:US/Canada banks,0.0008,0.0005,0.00025,0.0005,0.0005,0.07443,0.328135,0.04769,0.18755
+7,PNC,PNC Financial,bank,regional,US,0,A:US/Canada banks,0.0008,0.00025,0.0005,0.00175,0.0005,0.07443,0.32881,0.08795,0.15036
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diff --git a/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.json b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.json
new file mode 100644
index 0000000..3ae4ed0
--- /dev/null
+++ b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.json
@@ -0,0 +1,778 @@
+{
+  "summary": {
+    "n_entities": 38,
+    "n_samples_dataset": 4000,
+    "hw_n_unique_bitstrings": 140069,
+    "regimes": {
+      "baseline_mean_pd": 0.00233,
+      "stressed_target_mean_pd": 0.15,
+      "hw_observed_mean_pd": 0.30014
+    },
+    "generator_vs_hardware_structure": {
+      "bernoulli": {
+        "marginal_spearman_vs_hw": 0.3564,
+        "corr_offdiag_spearman_vs_hw": 0.0658,
+        "corr_offdiag_rmse_vs_hw": 0.1785,
+        "connectivity_spearman_vs_hw": -0.006,
+        "n_constant_cols": 1
+      },
+      "gaussian_copula": {
+        "marginal_spearman_vs_hw": 0.1413,
+        "corr_offdiag_spearman_vs_hw": -0.0676,
+        "corr_offdiag_rmse_vs_hw": 0.1804,
+        "connectivity_spearman_vs_hw": -0.0399,
+        "n_constant_cols": 4
+      },
+      "student_t_copula": {
+        "marginal_spearman_vs_hw": 0.3665,
+        "corr_offdiag_spearman_vs_hw": -0.049,
+        "corr_offdiag_rmse_vs_hw": 0.1803,
+        "connectivity_spearman_vs_hw": -0.0364,
+        "n_constant_cols": 2
+      },
+      "entangled_born_machine": {
+        "marginal_spearman_vs_hw": 0.456,
+        "corr_offdiag_spearman_vs_hw": 0.029,
+        "corr_offdiag_rmse_vs_hw": 0.1872,
+        "connectivity_spearman_vs_hw": 0.0395,
+        "n_constant_cols": 2
+      },
+      "baseline_target_spec": {
+        "corr_offdiag_spearman_vs_hw": 0.0314,
+        "corr_offdiag_rmse_vs_hw": 0.3429,
+        "connectivity_spearman_vs_hw": 0.141
+      }
+    },
+    "within_regime_marginal_rank_fidelity": {
+      "bernoulli": 0.7008,
+      "gaussian_copula": 0.6831,
+      "student_t_copula": 0.7717,
+      "entangled_born_machine": 0.6857,
+      "hw_vs_stressed_target": 0.3553
+    }
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+      "ticker": "BBD",
+      "name": "Banco Bradesco",
+      "node_type": "bank",
+      "business_type": "universal",
+      "region": "LATAM",
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+      "bloc": "B:Europe/UK+LatAm",
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+      "hw_mean_abs_corr": 0.0689
+    },
+    {
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+      "ticker": "AAPL",
+      "name": "Apple",
+      "node_type": "corporate",
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+      "region": "US",
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+      "hw_mean_abs_corr": 0.24562
+    },
+    {
+      "qubit": 29,
+      "ticker": "MSFT",
+      "name": "Microsoft",
+      "node_type": "corporate",
+      "business_type": "technology",
+      "region": "US",
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+      "ticker": "XOM",
+      "name": "ExxonMobil",
+      "node_type": "corporate",
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+      "hw_observed_pd": 0.259915,
+      "ebm_mean_abs_corr": 0.01034,
+      "hw_mean_abs_corr": 0.24594
+    },
+    {
+      "qubit": 31,
+      "ticker": "WMT",
+      "name": "Walmart",
+      "node_type": "corporate",
+      "business_type": "retail",
+      "region": "US",
+      "cluster": 2,
+      "bloc": "C:Japan+corporates",
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+      "ebm_mean_abs_corr": 0.01067,
+      "hw_mean_abs_corr": 0.19866
+    },
+    {
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+      "ticker": "BA",
+      "name": "Boeing",
+      "node_type": "corporate",
+      "business_type": "aerospace",
+      "region": "US",
+      "cluster": 1,
+      "bloc": "B:Europe/UK+LatAm",
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+      "ebm_mean_abs_corr": 0.00261,
+      "hw_mean_abs_corr": 0.13309
+    },
+    {
+      "qubit": 33,
+      "ticker": "T",
+      "name": "AT&T",
+      "node_type": "corporate",
+      "business_type": "telecom",
+      "region": "US",
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+      "hw_observed_pd": 0.35104,
+      "ebm_mean_abs_corr": 0.02147,
+      "hw_mean_abs_corr": 0.19507
+    },
+    {
+      "qubit": 34,
+      "ticker": "TM",
+      "name": "Toyota Motor",
+      "node_type": "corporate",
+      "business_type": "automotive",
+      "region": "JP",
+      "cluster": 2,
+      "bloc": "C:Japan+corporates",
+      "baseline_target_pd": 0.0008,
+      "ber_pd": 0.0015,
+      "gcop_pd": 0.00075,
+      "tcop_pd": 0.00075,
+      "ebm_pd": 0.00075,
+      "hw_stressed_target_pd": 0.07443,
+      "hw_observed_pd": 0.356105,
+      "ebm_mean_abs_corr": 0.001,
+      "hw_mean_abs_corr": 0.19643
+    },
+    {
+      "qubit": 35,
+      "ticker": "VWAGY",
+      "name": "Volkswagen",
+      "node_type": "corporate",
+      "business_type": "automotive",
+      "region": "EU",
+      "cluster": 1,
+      "bloc": "B:Europe/UK+LatAm",
+      "baseline_target_pd": 0.0031,
+      "ber_pd": 0.00375,
+      "gcop_pd": 0.0035,
+      "tcop_pd": 0.00325,
+      "ebm_pd": 0.00175,
+      "hw_stressed_target_pd": 0.23799,
+      "hw_observed_pd": 0.45663,
+      "ebm_mean_abs_corr": 0.02705,
+      "hw_mean_abs_corr": 0.12818
+    },
+    {
+      "qubit": 36,
+      "ticker": "SIEGY",
+      "name": "Siemens",
+      "node_type": "corporate",
+      "business_type": "industrial",
+      "region": "EU",
+      "cluster": 1,
+      "bloc": "B:Europe/UK+LatAm",
+      "baseline_target_pd": 0.0008,
+      "ber_pd": 0.00075,
+      "gcop_pd": 0.0,
+      "tcop_pd": 0.0015,
+      "ebm_pd": 0.001,
+      "hw_stressed_target_pd": 0.07443,
+      "hw_observed_pd": 0.420635,
+      "ebm_mean_abs_corr": 0.01123,
+      "hw_mean_abs_corr": 0.12846
+    },
+    {
+      "qubit": 37,
+      "ticker": "PBR",
+      "name": "Petrobras",
+      "node_type": "corporate",
+      "business_type": "energy",
+      "region": "LATAM",
+      "cluster": 1,
+      "bloc": "B:Europe/UK+LatAm",
+      "baseline_target_pd": 0.0139,
+      "ber_pd": 0.0125,
+      "gcop_pd": 0.01575,
+      "tcop_pd": 0.00975,
+      "ebm_pd": 0.014,
+      "hw_stressed_target_pd": 0.58605,
+      "hw_observed_pd": 0.688175,
+      "ebm_mean_abs_corr": 0.01877,
+      "hw_mean_abs_corr": 0.05206
+    }
+  ]
+}
\ No newline at end of file
diff --git a/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.png b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.png
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diff --git a/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.txt b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.txt
new file mode 100644
index 0000000..1c491dd
--- /dev/null
+++ b/outputs/real_cluster_mixture_stress_hw/entity_scenario_vs_hardware.txt
@@ -0,0 +1,45 @@
+PER-ENTITY: COMMITTED SCENARIO DATASET vs HARDWARE JOINT (ibm_boston)
+======================================================================
+Entities: 38 named real G-SIBs/corporates (one per qubit).
+Dataset : 4000 samples/generator on the BASELINE spec (mean PD 0.0023).
+Hardware: 140069 unique bitstrings on the STRESSED spec
+          (stressed target mean PD 0.150, observed 0.300 -- decohered up toward 0.5).
+
+MAGNITUDES ARE REGIME-MISMATCHED (baseline vs stressed); compare STRUCTURE.
+
+GENERATOR vs HARDWARE -- cross-entity structure agreement
+----------------------------------------------------------------------
+source                  marg-Spear  corr-Spear  corr-RMSE  conn-Spear
+bernoulli                   +0.356      +0.066      0.178      -0.006
+gaussian_copula             +0.141      -0.068      0.180      -0.040
+student_t_copula            +0.366      -0.049      0.180      -0.036
+entangled_born_machine      +0.456      +0.029      0.187      +0.040
+baseline_target_spec            --      +0.031      0.343      +0.141
+
+Rare-event wall: at ~0.2% PDs a co-default is essentially never observed in 4000 samples,
+so empirical pairwise correlations are noise (and 2/38 entities never default at all -> undefined, set 0).
+The 'baseline_target_spec' row uses the spec's INTENDED correlation instead -- the honest
+structural reference at baseline PDs. This is exactly what motivates stressing the spec.
+
+WITHIN-REGIME marginal rank fidelity (each source vs its OWN target)
+----------------------------------------------------------------------
+  bernoulli                        Spearman +0.701
+  gaussian_copula                  Spearman +0.683
+  student_t_copula                 Spearman +0.772
+  entangled_born_machine           Spearman +0.686
+  hw_vs_stressed_target            Spearman +0.355
+
+TOP-10 ENTITIES BY HARDWARE-OBSERVED CONNECTIVITY (mean |corr|)
+----------------------------------------------------------------------
+name                               clu  hw|corr|  ebm|corr|   hw_pd   ebm_pd
+Nomura Holdings                      2     0.259      0.021   0.289   0.0018
+ExxonMobil                           2     0.246      0.010   0.260   0.0005
+Apple                                2     0.246      0.001   0.267   0.0005
+Bank of New York Mellon              0     0.200      0.092   0.339   0.0008
+Walmart                              2     0.199      0.011   0.296   0.0010
+Toyota Motor                         2     0.196      0.001   0.356   0.0008
+AT&T                                 2     0.195      0.021   0.351   0.0030
+Citigroup                            0     0.195      0.027   0.252   0.0022
+Barclays                             1     0.191      0.064   0.185   0.0025
+Lloyds Banking Group                 1     0.190      0.036   0.247   0.0018
+
diff --git a/outputs/real_cluster_mixture_stress_hw/job_ids_live.json b/outputs/real_cluster_mixture_stress_hw/job_ids_live.json
new file mode 100644
index 0000000..0b8ae11
--- /dev/null
+++ b/outputs/real_cluster_mixture_stress_hw/job_ids_live.json
@@ -0,0 +1,38 @@
+{
+  "run": "200k-shot STRESSED real-network 3-cluster mixture on hardware",
+  "backend": "ibm_boston",
+  "shots_per_cluster": 200000,
+  "jobs": [
+    {
+      "cluster": 0,
+      "job_id": "d8ibcqpe8nrc73bi4ha0",
+      "backend": "ibm_boston",
+      "shots": 200000,
+      "shot_batches": [
+        100000,
+        100000
+      ]
+    },
+    {
+      "cluster": 1,
+      "job_id": "d8ibddc2upec739m0t80",
+      "backend": "ibm_boston",
+      "shots": 200000,
+      "shot_batches": [
+        100000,
+        100000
+      ]
+    },
+    {
+      "cluster": 2,
+      "job_id": "d8ibe3k2upec739m0tu0",
+      "backend": "ibm_boston",
+      "shots": 200000,
+      "shot_batches": [
+        100000,
+        100000
+      ]
+    }
+  ],
+  "note": "job ids persisted incrementally for resumability"
+}
\ No newline at end of file
diff --git a/outputs/real_cluster_mixture_stress_hw/joint_counts.csv b/outputs/real_cluster_mixture_stress_hw/joint_counts.csv
new file mode 100644
index 0000000..bdf022f
--- /dev/null
+++ b/outputs/real_cluster_mixture_stress_hw/joint_counts.csv
@@ -0,0 +1,140072 @@
+# bit order (left->right, qubit 0->37): JPM BAC C WFC GS MS USB PNC TFC BK STT HSBC BCS LYG DB SAN BBVA ING UBS MUFG SMFG MFG NMR RY TD BNS ITUB BBD AAPL MSFT XOM WMT BA T TM VWAGY SIEGY PBR
+# see qubit_legend.csv for the bank/company behind each position
+bitstring,count,probability,n_defaults
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diff --git a/outputs/real_cluster_mixture_stress_hw/joint_counts_hist.png b/outputs/real_cluster_mixture_stress_hw/joint_counts_hist.png
new file mode 100644
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diff --git a/outputs/real_cluster_mixture_stress_hw/plan.json b/outputs/real_cluster_mixture_stress_hw/plan.json
new file mode 100644
index 0000000..b20fbbd
--- /dev/null
+++ b/outputs/real_cluster_mixture_stress_hw/plan.json
@@ -0,0 +1,233 @@
+{
+  "system": "REAL 38-entity exposure network -- 2008 STRESS, DENSE blocks on hardware",
+  "n": 38,
+  "backend": "ibm_boston",
+  "partition_source": "/Users/alex/projects/quantum-hack-espoo/outputs/data_network/real_network_partition.json",
+  "partition_content_hash": "fd2eb8b2af61d2771416f1f169506d78b2f92604f15bf32ac01ebe3ba55ad855",
+  "cluster_sizes": [
+    14,
+    14,
+    10
+  ],
+  "labels": [
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    2,
+    2,
+    2,
+    2,
+    0,
+    0,
+    0,
+    1,
+    1,
+    2,
+    2,
+    2,
+    2,
+    1,
+    2,
+    2,
+    1,
+    1,
+    1
+  ],
+  "stress_calibration": {
+    "scenario": "2008-style systemic credit crisis (uniform logit-shift overlay)",
+    "anchor": "Moody's GFC-peak default rates (all-corporate ~5%, speculative-grade ~13%, long-run all-rated 1.48-1.79%) + FRED BAA-AAA spread GFC/calm multiplier ~3.85x",
+    "target_mean_pd": 0.15,
+    "crisis_floor": 0.027,
+    "logit_shift": 4.609529,
+    "gfc_spread_multiplier": 3.85,
+    "baseline_mean_pd": 0.002332,
+    "stressed_mean_pd": 0.15,
+    "stressed_min_pd": 0.027,
+    "stressed_max_pd": 0.58605,
+    "noise_floor": 0.027,
+    "n_entities": 38,
+    "n_above_noise_floor": 38,
+    "n_below_noise_floor": 0,
+    "correlation_unchanged": true,
+    "ordering_preserved": true,
+    "caveat": "Hypothetical uniform crisis overlay calibrated to historical aggregates, not an entity-by-entity 2008 re-rating; assumes a broad systemic shock."
+  },
+  "loadability": {
+    "noise_floor": 0.027,
+    "baseline_mean_pd": 0.002332,
+    "baseline_n_above_floor": 0,
+    "stressed_mean_pd": 0.15,
+    "stressed_min_pd": 0.027,
+    "stressed_max_pd": 0.58605,
+    "stressed_n_above_floor": 38,
+    "n_entities": 38,
+    "snr_min": 1.0,
+    "snr_mean": 5.556,
+    "all_clear_floor": true
+  },
+  "noise_floor_assumed": 0.027,
+  "pd_inspection": [
+    {
+      "cluster": 0,
+      "members": [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7,
+        8,
+        9,
+        10,
+        23,
+        24,
+        25
+      ],
+      "pd_min": 0.056869,
+      "pd_max": 0.237992,
+      "pd_mean": 0.10697,
+      "n_above_noise_floor": 14,
+      "all_above_noise_floor": true
+    },
+    {
+      "cluster": 1,
+      "members": [
+        11,
+        12,
+        13,
+        14,
+        15,
+        16,
+        17,
+        18,
+        26,
+        27,
+        32,
+        35,
+        36,
+        37
+      ],
+      "pd_min": 0.074429,
+      "pd_max": 0.58605,
+      "pd_mean": 0.230794,
+      "n_above_noise_floor": 14,
+      "all_above_noise_floor": true
+    },
+    {
+      "cluster": 2,
+      "members": [
+        19,
+        20,
+        21,
+        22,
+        28,
+        29,
+        30,
+        31,
+        33,
+        34
+      ],
+      "pd_min": 0.027,
+      "pd_max": 0.237992,
+      "pd_mean": 0.097131,
+      "n_above_noise_floor": 10,
+      "all_above_noise_floor": true
+    }
+  ],
+  "shot_plan": {
+    "total_shots_per_cluster": 200000,
+    "max_shots_per_job": "read from backend at submit time",
+    "note": "batches computed live from backend.configuration().max_shots"
+  },
+  "per_cluster": [
+    {
+      "cluster": 0,
+      "members": [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7,
+        8,
+        9,
+        10,
+        23,
+        24,
+        25
+      ],
+      "qubits": 14,
+      "entanglers": 91,
+      "approx_two_qubit_gates_pretranspile": 182,
+      "entanglement_depth_pretranspile": 39,
+      "target_within_abs_corr": 0.3647,
+      "target_pd_mean": 0.107
+    },
+    {
+      "cluster": 1,
+      "members": [
+        11,
+        12,
+        13,
+        14,
+        15,
+        16,
+        17,
+        18,
+        26,
+        27,
+        32,
+        35,
+        36,
+        37
+      ],
+      "qubits": 14,
+      "entanglers": 91,
+      "approx_two_qubit_gates_pretranspile": 182,
+      "entanglement_depth_pretranspile": 44,
+      "target_within_abs_corr": 0.2526,
+      "target_pd_mean": 0.2308
+    },
+    {
+      "cluster": 2,
+      "members": [
+        19,
+        20,
+        21,
+        22,
+        28,
+        29,
+        30,
+        31,
+        33,
+        34
+      ],
+      "qubits": 10,
+      "entanglers": 45,
+      "approx_two_qubit_gates_pretranspile": 90,
+      "entanglement_depth_pretranspile": 27,
+      "target_within_abs_corr": 0.1363,
+      "target_pd_mean": 0.0971
+    }
+  ]
+}
\ No newline at end of file
diff --git a/outputs/real_cluster_mixture_stress_hw/qubit_legend.csv b/outputs/real_cluster_mixture_stress_hw/qubit_legend.csv
new file mode 100644
index 0000000..e3f4808
--- /dev/null
+++ b/outputs/real_cluster_mixture_stress_hw/qubit_legend.csv
@@ -0,0 +1,39 @@
+qubit,ticker,name,node_type,business_type,region,cluster,bloc
+0,JPM,"JPMorgan Chase",bank,universal,US,0,A:US/Canada banks
+1,BAC,"Bank of America",bank,universal,US,0,A:US/Canada banks
+2,C,"Citigroup",bank,universal,US,0,A:US/Canada banks
+3,WFC,"Wells Fargo",bank,universal,US,0,A:US/Canada banks
+4,GS,"Goldman Sachs",bank,investment,US,0,A:US/Canada banks
+5,MS,"Morgan Stanley",bank,investment,US,0,A:US/Canada banks
+6,USB,"U.S. Bancorp",bank,regional,US,0,A:US/Canada banks
+7,PNC,"PNC Financial",bank,regional,US,0,A:US/Canada banks
+8,TFC,"Truist Financial",bank,regional,US,0,A:US/Canada banks
+9,BK,"Bank of New York Mellon",bank,custodian,US,0,A:US/Canada banks
+10,STT,"State Street",bank,custodian,US,0,A:US/Canada banks
+11,HSBC,"HSBC Holdings",bank,universal,UK,1,B:Europe/UK+LatAm
+12,BCS,"Barclays",bank,universal,UK,1,B:Europe/UK+LatAm
+13,LYG,"Lloyds Banking Group",bank,regional,UK,1,B:Europe/UK+LatAm
+14,DB,"Deutsche Bank",bank,universal,EU,1,B:Europe/UK+LatAm
+15,SAN,"Banco Santander",bank,universal,EU,1,B:Europe/UK+LatAm
+16,BBVA,"Banco Bilbao Vizcaya Argentaria",bank,universal,EU,1,B:Europe/UK+LatAm
+17,ING,"ING Groep",bank,universal,EU,1,B:Europe/UK+LatAm
+18,UBS,"UBS Group",bank,investment,EU,1,B:Europe/UK+LatAm
+19,MUFG,"Mitsubishi UFJ Financial Group",bank,universal,JP,2,C:Japan+corporates
+20,SMFG,"Sumitomo Mitsui Financial Group",bank,universal,JP,2,C:Japan+corporates
+21,MFG,"Mizuho Financial Group",bank,universal,JP,2,C:Japan+corporates
+22,NMR,"Nomura Holdings",bank,investment,JP,2,C:Japan+corporates
+23,RY,"Royal Bank of Canada",bank,universal,CA,0,A:US/Canada banks
+24,TD,"Toronto-Dominion Bank",bank,universal,CA,0,A:US/Canada banks
+25,BNS,"Bank of Nova Scotia",bank,universal,CA,0,A:US/Canada banks
+26,ITUB,"Itau Unibanco",bank,universal,LATAM,1,B:Europe/UK+LatAm
+27,BBD,"Banco Bradesco",bank,universal,LATAM,1,B:Europe/UK+LatAm
+28,AAPL,"Apple",corporate,technology,US,2,C:Japan+corporates
+29,MSFT,"Microsoft",corporate,technology,US,2,C:Japan+corporates
+30,XOM,"ExxonMobil",corporate,energy,US,2,C:Japan+corporates
+31,WMT,"Walmart",corporate,retail,US,2,C:Japan+corporates
+32,BA,"Boeing",corporate,aerospace,US,1,B:Europe/UK+LatAm
+33,T,"AT&T",corporate,telecom,US,2,C:Japan+corporates
+34,TM,"Toyota Motor",corporate,automotive,JP,2,C:Japan+corporates
+35,VWAGY,"Volkswagen",corporate,automotive,EU,1,B:Europe/UK+LatAm
+36,SIEGY,"Siemens",corporate,industrial,EU,1,B:Europe/UK+LatAm
+37,PBR,"Petrobras",corporate,energy,LATAM,1,B:Europe/UK+LatAm
diff --git a/outputs/real_cluster_mixture_stress_hw/qubit_legend.json b/outputs/real_cluster_mixture_stress_hw/qubit_legend.json
new file mode 100644
index 0000000..03a7b21
--- /dev/null
+++ b/outputs/real_cluster_mixture_stress_hw/qubit_legend.json
@@ -0,0 +1,382 @@
+[
+  {
+    "qubit": 0,
+    "ticker": "JPM",
+    "name": "JPMorgan Chase",
+    "node_type": "bank",
+    "business_type": "universal",
+    "region": "US",
+    "cluster": 0,
+    "bloc": "A:US/Canada banks"
+  },
+  {
+    "qubit": 1,
+    "ticker": "BAC",
+    "name": "Bank of America",
+    "node_type": "bank",
+    "business_type": "universal",
+    "region": "US",
+    "cluster": 0,
+    "bloc": "A:US/Canada banks"
+  },
+  {
+    "qubit": 2,
+    "ticker": "C",
+    "name": "Citigroup",
+    "node_type": "bank",
+    "business_type": "universal",
+    "region": "US",
+    "cluster": 0,
+    "bloc": "A:US/Canada banks"
+  },
+  {
+    "qubit": 3,
+    "ticker": "WFC",
+    "name": "Wells Fargo",
+    "node_type": "bank",
+    "business_type": "universal",
+    "region": "US",
+    "cluster": 0,
+    "bloc": "A:US/Canada banks"
+  },
+  {
+    "qubit": 4,
+    "ticker": "GS",
+    "name": "Goldman Sachs",
+    "node_type": "bank",
+    "business_type": "investment",
+    "region": "US",
+    "cluster": 0,
+    "bloc": "A:US/Canada banks"
+  },
+  {
+    "qubit": 5,
+    "ticker": "MS",
+    "name": "Morgan Stanley",
+    "node_type": "bank",
+    "business_type": "investment",
+    "region": "US",
+    "cluster": 0,
+    "bloc": "A:US/Canada banks"
+  },
+  {
+    "qubit": 6,
+    "ticker": "USB",
+    "name": "U.S. Bancorp",
+    "node_type": "bank",
+    "business_type": "regional",
+    "region": "US",
+    "cluster": 0,
+    "bloc": "A:US/Canada banks"
+  },
+  {
+    "qubit": 7,
+    "ticker": "PNC",
+    "name": "PNC Financial",
+    "node_type": "bank",
+    "business_type": "regional",
+    "region": "US",
+    "cluster": 0,
+    "bloc": "A:US/Canada banks"
+  },
+  {
+    "qubit": 8,
+    "ticker": "TFC",
+    "name": "Truist Financial",
+    "node_type": "bank",
+    "business_type": "regional",
+    "region": "US",
+    "cluster": 0,
+    "bloc": "A:US/Canada banks"
+  },
+  {
+    "qubit": 9,
+    "ticker": "BK",
+    "name": "Bank of New York Mellon",
+    "node_type": "bank",
+    "business_type": "custodian",
+    "region": "US",
+    "cluster": 0,
+    "bloc": "A:US/Canada banks"
+  },
+  {
+    "qubit": 10,
+    "ticker": "STT",
+    "name": "State Street",
+    "node_type": "bank",
+    "business_type": "custodian",
+    "region": "US",
+    "cluster": 0,
+    "bloc": "A:US/Canada banks"
+  },
+  {
+    "qubit": 11,
+    "ticker": "HSBC",
+    "name": "HSBC Holdings",
+    "node_type": "bank",
+    "business_type": "universal",
+    "region": "UK",
+    "cluster": 1,
+    "bloc": "B:Europe/UK+LatAm"
+  },
+  {
+    "qubit": 12,
+    "ticker": "BCS",
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\ No newline at end of file
diff --git a/outputs/real_cluster_mixture_stress_hw/real_stress_hardware_run_report.json b/outputs/real_cluster_mixture_stress_hw/real_stress_hardware_run_report.json
new file mode 100644
index 0000000..bfdd839
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diff --git a/outputs/real_cluster_mixture_stress_hw/stressed_spec.json b/outputs/real_cluster_mixture_stress_hw/stressed_spec.json
new file mode 100644
index 0000000..1a5b90f
--- /dev/null
+++ b/outputs/real_cluster_mixture_stress_hw/stressed_spec.json
@@ -0,0 +1,3594 @@
+{
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+    ]
+  ],
+  "target_joint_probs": null,
+  "clusters": [
+    "community_0",
+    "community_0",
+    "community_0",
+    "community_0",
+    "community_0",
+    "community_0",
+    "community_0",
+    "community_0",
+    "community_0",
+    "community_0",
+    "community_0",
+    "community_1",
+    "community_1",
+    "community_1",
+    "community_1",
+    "community_1",
+    "community_1",
+    "community_1",
+    "community_1",
+    "community_2",
+    "community_2",
+    "community_2",
+    "community_2",
+    "community_0",
+    "community_0",
+    "community_0",
+    "community_3",
+    "community_3",
+    "community_4",
+    "community_4",
+    "community_3",
+    "community_4",
+    "community_4",
+    "community_0",
+    "community_2",
+    "community_1",
+    "community_1",
+    "community_3"
+  ],
+  "metadata": {
+    "name": "Bank exposure network (real anchor) -- 2008 stress (mean PD 15%)",
+    "correlation_space": "latent_gaussian",
+    "source": "Real anchor: roster of large banks + non-financial corporates (data/external/banks/gsib_roster.csv). Marginals: Moody's Corporate Default & Recovery Rates 1920-2004, Exhibit 17 (whole-letter, Year-1). Correlation: Yahoo Finance daily adjusted close, log-return correlation (2021-06-01..2024-06-01).",
+    "content_hash": "fd2eb8b2af61d2771416f1f169506d78b2f92604f15bf32ac01ebe3ba55ad855",
+    "reconstruction_method": "max_entropy+risk_adjusted",
+    "reconstruction_params": {
+      "single_counterparty_cap_frac": 0.25,
+      "single_counterparty_cap_applied": 1.0,
+      "rollover_beta": 0.6,
+      "rollover_tau": 1.0,
+      "wrong_way_gamma": 0.5,
+      "concentration_delta": 0.5,
+      "exposure_is": "effective_loss",
+      "edge_channels": "lgd_recovery|maturity_rollover|wrong_way|substitutability"
+    },
+    "fit_params": {
+      "interbank_share": 0.2,
+      "buffer_ratio": 0.06,
+      "single_counterparty_cap_frac": 0.25,
+      "reconstruction_method": "max_entropy",
+      "cluster_threshold": 0.0,
+      "correlation_space": "latent_gaussian",
+      "n_nodes": 38,
+      "n_corporates": 10,
+      "n_financials": 28,
+      "n_communities": 5,
+      "modularity": 0.0301,
+      "cluster_mean_ari": 0.846,
+      "cluster_min_ari": 0.7975,
+      "cluster_stable": true,
+      "equity_window": "2021-06-01..2024-06-01",
+      "equity_n_obs": 755,
+      "edge_channels": "lgd_recovery|maturity_rollover|wrong_way|substitutability",
+      "effective_to_notional_ratio": 0.8963,
+      "rollover_beta": 0.6,
+      "rollover_tau": 1.0,
+      "wrong_way_gamma": 0.5,
+      "concentration_delta": 0.5
+    },
+    "node_ids": [
+      "JPM",
+      "BAC",
+      "C",
+      "WFC",
+      "GS",
+      "MS",
+      "USB",
+      "PNC",
+      "TFC",
+      "BK",
+      "STT",
+      "HSBC",
+      "BCS",
+      "LYG",
+      "DB",
+      "SAN",
+      "BBVA",
+      "ING",
+      "UBS",
+      "MUFG",
+      "SMFG",
+      "MFG",
+      "NMR",
+      "RY",
+      "TD",
+      "BNS",
+      "ITUB",
+      "BBD",
+      "AAPL",
+      "MSFT",
+      "XOM",
+      "WMT",
+      "BA",
+      "T",
+      "TM",
+      "VWAGY",
+      "SIEGY",
+      "PBR"
+    ],
+    "ratings": [
+      "A-",
+      "A-",
+      "BBB+",
+      "BBB+",
+      "BBB+",
+      "A-",
+      "A",
+      "A-",
+      "A-",
+      "A",
+      "A",
+      "A+",
+      "BBB+",
+      "BBB+",
+      "A-",
+      "A+",
+      "A",
+      "A+",
+      "A-",
+      "A",
+      "A",
+      "A",
+      "BBB+",
+      "AA-",
+      "AA-",
+      "A+",
+      "BB-",
+      "BB-",
+      "AA+",
+      "AAA",
+      "AA-",
+      "AA",
+      "BBB-",
+      "BBB",
+      "A+",
+      "BBB+",
+      "A+",
+      "BB"
+    ],
+    "regions": [
+      "US",
+      "US",
+      "US",
+      "US",
+      "US",
+      "US",
+      "US",
+      "US",
+      "US",
+      "US",
+      "US",
+      "UK",
+      "UK",
+      "UK",
+      "EU",
+      "EU",
+      "EU",
+      "EU",
+      "EU",
+      "JP",
+      "JP",
+      "JP",
+      "JP",
+      "CA",
+      "CA",
+      "CA",
+      "LATAM",
+      "LATAM",
+      "US",
+      "US",
+      "US",
+      "US",
+      "US",
+      "US",
+      "JP",
+      "EU",
+      "EU",
+      "LATAM"
+    ],
+    "business_types": [
+      "universal",
+      "universal",
+      "universal",
+      "universal",
+      "investment",
+      "investment",
+      "regional",
+      "regional",
+      "regional",
+      "custodian",
+      "custodian",
+      "universal",
+      "universal",
+      "regional",
+      "universal",
+      "universal",
+      "universal",
+      "universal",
+      "investment",
+      "universal",
+      "universal",
+      "universal",
+      "investment",
+      "universal",
+      "universal",
+      "universal",
+      "universal",
+      "universal",
+      "technology",
+      "technology",
+      "energy",
+      "retail",
+      "aerospace",
+      "telecom",
+      "automotive",
+      "automotive",
+      "industrial",
+      "energy"
+    ],
+    "n_communities": 5,
+    "feature_schema": {
+      "fields": [
+        {
+          "name": "node_id",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Stable institution identifier.",
+          "visible_to": [
+            "generator",
+            "simulator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "name",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Human-readable institution name.",
+          "visible_to": [
+            "visualization"
+          ]
+        },
+        {
+          "name": "node_type",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Institution class (bank/insurer/...).",
+          "visible_to": [
+            "generator",
+            "simulator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "business_type",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Business model (universal/investment/regional/custodian).",
+          "visible_to": [
+            "visualization"
+          ]
+        },
+        {
+          "name": "region",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Geographic region.",
+          "visible_to": [
+            "visualization"
+          ]
+        },
+        {
+          "name": "rating",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Public S&P long-term issuer rating.",
+          "visible_to": [
+            "visualization"
+          ]
+        },
+        {
+          "name": "marginal_default_prob",
+          "level": "node",
+          "dtype": "float",
+          "description": "p_i: 1-year marginal default probability from rating.",
+          "visible_to": [
+            "generator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "capital_buffer",
+          "level": "node",
+          "dtype": "float",
+          "description": "Tier-1-style loss-absorbing buffer used by the cascade.",
+          "visible_to": [
+            "simulator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "interbank_assets",
+          "level": "node",
+          "dtype": "float",
+          "description": "Total interbank claims (row-sum constraint for edges).",
+          "visible_to": []
+        },
+        {
+          "name": "interbank_liabilities",
+          "level": "node",
+          "dtype": "float",
+          "description": "Total interbank liabilities (col-sum constraint for edges).",
+          "visible_to": []
+        },
+        {
+          "name": "correlation_matrix",
+          "level": "system",
+          "dtype": "matrix",
+          "description": "Equity-return latent correlation (copula dependency target).",
+          "visible_to": [
+            "generator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "exposure_matrix",
+          "level": "edge",
+          "dtype": "matrix",
+          "description": "W[i,j] = loss to i if j defaults (reconstructed bilateral).",
+          "visible_to": [
+            "simulator",
+            "visualization"
+          ]
+        },
+        {
+          "name": "cluster",
+          "level": "node",
+          "dtype": "categorical",
+          "description": "Community label from detection on the network.",
+          "visible_to": [
+            "visualization"
+          ]
+        }
+      ]
+    },
+    "provenance_notes": "Bilateral exposures are reconstructed (real bilateral data is confidential) and risk-adjusted into an effective-loss matrix (LGD/maturity/wrong-way/substitutability); marginals and equity correlation are empirical. Corporates borrow from banks but do not lend interbank (directed bank->corporate edges).",
+    "stress": {
+      "scenario": "2008-style systemic credit crisis (uniform logit-shift overlay)",
+      "anchor": "Moody's GFC-peak default rates (all-corporate ~5%, speculative-grade ~13%, long-run all-rated 1.48-1.79%) + FRED BAA-AAA spread GFC/calm multiplier ~3.85x",
+      "target_mean_pd": 0.15,
+      "crisis_floor": 0.027,
+      "logit_shift": 4.609529,
+      "gfc_spread_multiplier": 3.85,
+      "baseline_mean_pd": 0.002332,
+      "stressed_mean_pd": 0.15,
+      "stressed_min_pd": 0.027,
+      "stressed_max_pd": 0.58605,
+      "noise_floor": 0.027,
+      "n_entities": 38,
+      "n_above_noise_floor": 38,
+      "n_below_noise_floor": 0,
+      "correlation_unchanged": true,
+      "ordering_preserved": true,
+      "caveat": "Hypothetical uniform crisis overlay calibrated to historical aggregates, not an entity-by-entity 2008 re-rating; assumes a broad systemic shock."
+    }
+  }
+}
\ No newline at end of file
diff --git a/scripts/analyze_entity_scenario_vs_hardware.py b/scripts/analyze_entity_scenario_vs_hardware.py
new file mode 100644
index 0000000..d7f5e62
--- /dev/null
+++ b/scripts/analyze_entity_scenario_vs_hardware.py
@@ -0,0 +1,423 @@
+"""Per-named-entity comparison: committed scenario dataset vs the hardware joint counts.
+
+Joins three things on a single named-entity axis (the 38 real G-SIBs/corporates):
+
+  1. ``data/scenario_dataset/real_gsib/`` -- the committed, pre-sampled scenarios
+     from every generator (bernoulli, gaussian_copula, student_t_copula,
+     entangled_born_machine) on the BASELINE real network spec, plus that spec's
+     target marginals/correlation.
+  2. ``outputs/real_cluster_mixture_stress_hw/joint_counts.csv`` -- the reconciled
+     global joint distribution actually measured on ibm_boston (STRESSED spec).
+  3. ``.../qubit_legend.csv`` and the run report -- entity names per qubit and the
+     stressed loaded target marginal each qubit was asked to reproduce.
+
+It is deliberately independent of ``analyze_real_cluster_mixture_stress_hw.py``:
+that script does a 4-way cascade/fidelity comparison; this one produces a flat
+per-entity table (marginals + correlation connectivity, named) and the structural
+similarity between each dataset generator and the hardware joint.
+
+Important: the committed dataset is on the BASELINE spec (~0.2% PDs) while the
+hardware run is on the STRESSED spec (~15% target, decohered up toward 0.5). The
+magnitudes are therefore NOT comparable -- what is comparable is the cross-entity
+STRUCTURE (rank ordering of who is riskiest / most connected). The script reports
+both, and labels the magnitude columns by their regime so nothing is conflated.
+
+Run:
+    uv run python scripts/analyze_entity_scenario_vs_hardware.py
+
+Writes, into outputs/real_cluster_mixture_stress_hw/:
+    entity_scenario_vs_hardware.csv     # the per-entity table
+    entity_scenario_vs_hardware.json    # table + summary similarity metrics
+    entity_scenario_vs_hardware.txt     # human-readable summary
+    entity_scenario_vs_hardware.png     # marginals + connectivity figure
+"""
+
+from __future__ import annotations
+
+import csv
+import json
+from pathlib import Path
+
+import numpy as np
+
+REPO = Path(__file__).resolve().parent.parent
+DATASET_DIR = REPO / "data" / "scenario_dataset" / "real_gsib"
+HW_DIR = REPO / "outputs" / "real_cluster_mixture_stress_hw"
+LEGEND_CSV = HW_DIR / "qubit_legend.csv"
+JOINT_CSV = HW_DIR / "joint_counts.csv"
+REPORT_JSON = HW_DIR / "real_stress_hardware_run_report.json"
+
+GENERATORS = ["bernoulli", "gaussian_copula", "student_t_copula", "entangled_born_machine"]
+GEN_SHORT = {
+    "bernoulli": "ber",
+    "gaussian_copula": "gcop",
+    "student_t_copula": "tcop",
+    "entangled_born_machine": "ebm",
+}
+
+
+# --------------------------------------------------------------------------- io
+
+def load_legend() -> list[dict]:
+    """qubit -> entity metadata, ordered by qubit index 0..37."""
+    rows = []
+    with LEGEND_CSV.open() as fh:
+        for r in csv.DictReader(fh):
+            r["qubit"] = int(r["qubit"])
+            r["cluster"] = int(r["cluster"])
+            rows.append(r)
+    rows.sort(key=lambda r: r["qubit"])
+    return rows
+
+
+def safe_corr(samples: np.ndarray) -> np.ndarray:
+    """corrcoef with constant (never-default) columns mapped to 0 instead of NaN.
+
+    At baseline ~0.2% PDs many entities never default in 4000 samples, so their
+    empirical column is constant -> correlation undefined. This is the rare-event
+    wall; we report it as zero measurable correlation rather than propagate NaN.
+    """
+    with np.errstate(divide="ignore", invalid="ignore"):
+        c = np.corrcoef(samples, rowvar=False)
+    c = np.nan_to_num(c, nan=0.0)
+    np.fill_diagonal(c, 1.0)
+    return c
+
+
+def load_dataset_generator(gen: str) -> dict:
+    npz = np.load(DATASET_DIR / f"real_gsib__{gen}.npz", allow_pickle=True)
+    samples = npz["samples"].astype(float)  # (n_samples, 38)
+    # fraction of entities with an undefined empirical correlation (constant column)
+    n_constant = int(np.sum(samples.std(axis=0) == 0))
+    return {
+        "samples": samples,
+        "marginals": samples.mean(axis=0),
+        "corr": safe_corr(samples),
+        "n_constant_cols": n_constant,
+        "target_marginals": npz["target_marginals"].astype(float),
+        "target_corr": npz["target_corr"].astype(float),
+        "node_names": [str(x) for x in npz["node_names"]],
+    }
+
+
+def load_hardware_joint(n_qubits: int) -> dict:
+    """Weighted marginals + correlation from the reconciled global joint counts.
+
+    Bit order is left->right = qubit 0..37 (per the file's header comment); we
+    verify that against the n_defaults column on the first rows.
+    """
+    bits_rows: list[np.ndarray] = []
+    weights: list[float] = []
+    checked = 0
+    with JOINT_CSV.open() as fh:
+        for line in fh:
+            if line.startswith("#") or line.startswith("bitstring"):
+                continue
+            bitstring, count, prob, n_def = line.rstrip("\n").split(",")
+            if len(bitstring) != n_qubits:
+                raise ValueError(f"bitstring width {len(bitstring)} != {n_qubits}")
+            arr = np.frombuffer(bitstring.encode(), dtype=np.uint8) - ord("0")
+            if checked < 50:
+                assert int(arr.sum()) == int(n_def), "bit order / n_defaults mismatch"
+                checked += 1
+            bits_rows.append(arr.astype(float))
+            weights.append(float(prob))
+
+    X = np.vstack(bits_rows)              # (n_unique, 38)
+    w = np.asarray(weights)
+    w = w / w.sum()                       # renormalise (counts -> probabilities)
+
+    ex = w @ X                            # E[x_i]
+    exx = X.T @ (w[:, None] * X)          # E[x_i x_j]
+    cov = exx - np.outer(ex, ex)
+    var = ex - ex * ex                    # binary variance E[x]-E[x]^2
+    denom = np.sqrt(np.outer(var, var))
+    with np.errstate(divide="ignore", invalid="ignore"):
+        corr = np.where(denom > 0, cov / denom, 0.0)
+    np.fill_diagonal(corr, 1.0)
+    return {"marginals": ex, "corr": corr, "n_unique": X.shape[0]}
+
+
+def load_stressed_target(n_qubits: int) -> np.ndarray:
+    """Loaded stressed target marginal per global qubit, from the run report."""
+    report = json.loads(REPORT_JSON.read_text())
+    tgt = np.full(n_qubits, np.nan)
+    for c in report["hardware_per_cluster"]:
+        for q, m in zip(c["members"], c["target_marginals"]):
+            tgt[q] = float(m)
+    return tgt
+
+
+# ------------------------------------------------------------------ structure
+
+def off_diagonal(m: np.ndarray) -> np.ndarray:
+    iu = np.triu_indices_from(m, k=1)
+    return m[iu]
+
+
+def spearman(a: np.ndarray, b: np.ndarray) -> float:
+    a = np.asarray(a, float)
+    b = np.asarray(b, float)
+    mask = np.isfinite(a) & np.isfinite(b)
+    if mask.sum() < 2:
+        return float("nan")
+    a, b = a[mask], b[mask]
+    if np.ptp(a) == 0 or np.ptp(b) == 0:
+        return float("nan")
+    ra = np.argsort(np.argsort(a))
+    rb = np.argsort(np.argsort(b))
+    return float(np.corrcoef(ra, rb)[0, 1])
+
+
+def mean_abs_offdiag_corr(corr: np.ndarray) -> np.ndarray:
+    """Per-entity connectivity: mean |corr| to all other entities."""
+    a = np.abs(corr).copy()
+    np.fill_diagonal(a, 0.0)
+    return a.sum(axis=1) / (a.shape[1] - 1)
+
+
+# ----------------------------------------------------------------------- main
+
+def main() -> None:
+    legend = load_legend()
+    n = len(legend)
+    names = [r["name"] for r in legend]
+
+    data = {gen: load_dataset_generator(gen) for gen in GENERATORS}
+    # the baseline target is identical across generators (same spec)
+    baseline_target = data[GENERATORS[0]]["target_marginals"]
+    baseline_target_corr = data[GENERATORS[0]]["target_corr"]
+
+    # sanity: dataset node order must match the legend's qubit order
+    if data[GENERATORS[0]]["node_names"] != names:
+        raise ValueError("dataset node_names do not match qubit_legend order")
+
+    hw = load_hardware_joint(n)
+    stressed_target = load_stressed_target(n)
+
+    # ---- per-entity connectivity (mean |corr|) for each source
+    conn = {gen: mean_abs_offdiag_corr(data[gen]["corr"]) for gen in GENERATORS}
+    conn["hw"] = mean_abs_offdiag_corr(hw["corr"])
+    # the spec's intended correlation structure -- well-defined even where the
+    # baseline empirical correlations collapse to noise (rare-event wall)
+    conn["baseline_target"] = mean_abs_offdiag_corr(baseline_target_corr)
+
+    # ---- per-entity table
+    table = []
+    for i, r in enumerate(legend):
+        row = {
+            "qubit": r["qubit"],
+            "ticker": r["ticker"],
+            "name": r["name"],
+            "node_type": r["node_type"],
+            "business_type": r["business_type"],
+            "region": r["region"],
+            "cluster": r["cluster"],
+            "bloc": r["bloc"],
+            # BASELINE regime (committed dataset)
+            "baseline_target_pd": round(float(baseline_target[i]), 6),
+        }
+        for gen in GENERATORS:
+            row[f"{GEN_SHORT[gen]}_pd"] = round(float(data[gen]["marginals"][i]), 6)
+        # STRESSED / hardware regime
+        row["hw_stressed_target_pd"] = round(float(stressed_target[i]), 6)
+        row["hw_observed_pd"] = round(float(hw["marginals"][i]), 6)
+        # connectivity (structure)
+        row["ebm_mean_abs_corr"] = round(float(conn["entangled_born_machine"][i]), 5)
+        row["hw_mean_abs_corr"] = round(float(conn["hw"][i]), 5)
+        table.append(row)
+
+    # ---- structural similarity: each generator's joint vs the hardware joint
+    hw_off = off_diagonal(hw["corr"])
+    similarity = {}
+    for gen in GENERATORS:
+        gen_off = off_diagonal(data[gen]["corr"])
+        similarity[gen] = {
+            # marginal rank agreement with hardware (magnitude is regime-mismatched)
+            "marginal_spearman_vs_hw": round(
+                spearman(data[gen]["marginals"], hw["marginals"]), 4),
+            # correlation-structure agreement with hardware
+            "corr_offdiag_spearman_vs_hw": round(spearman(gen_off, hw_off), 4),
+            "corr_offdiag_rmse_vs_hw": round(
+                float(np.sqrt(np.mean((gen_off - hw_off) ** 2))), 4),
+            # connectivity-ranking agreement with hardware
+            "connectivity_spearman_vs_hw": round(
+                spearman(conn[gen], conn["hw"]), 4),
+            # how many entities had no measurable empirical correlation (rare-event wall)
+            "n_constant_cols": data[gen]["n_constant_cols"],
+        }
+    # the SPEC's intended structure (not its noisy empirical sample) vs hardware --
+    # the meaningful structural reference, since baseline empirical corr is rare-event noise
+    target_off = off_diagonal(baseline_target_corr)
+    similarity["baseline_target_spec"] = {
+        "corr_offdiag_spearman_vs_hw": round(spearman(target_off, hw_off), 4),
+        "corr_offdiag_rmse_vs_hw": round(
+            float(np.sqrt(np.mean((target_off - hw_off) ** 2))), 4),
+        "connectivity_spearman_vs_hw": round(
+            spearman(conn["baseline_target"], conn["hw"]), 4),
+    }
+
+    # marginal rank agreement: dataset generators vs their own baseline target,
+    # and hardware-observed vs its own stressed target (within-regime fidelity)
+    within_regime = {
+        gen: round(spearman(data[gen]["marginals"], baseline_target), 4)
+        for gen in GENERATORS
+    }
+    within_regime["hw_vs_stressed_target"] = round(
+        spearman(hw["marginals"], stressed_target), 4)
+
+    summary = {
+        "n_entities": n,
+        "n_samples_dataset": int(data[GENERATORS[0]]["samples"].shape[0]),
+        "hw_n_unique_bitstrings": int(hw["n_unique"]),
+        "regimes": {
+            "baseline_mean_pd": round(float(baseline_target.mean()), 5),
+            "stressed_target_mean_pd": round(float(np.nanmean(stressed_target)), 5),
+            "hw_observed_mean_pd": round(float(hw["marginals"].mean()), 5),
+        },
+        "generator_vs_hardware_structure": similarity,
+        "within_regime_marginal_rank_fidelity": within_regime,
+    }
+
+    out_json = {"summary": summary, "entities": table}
+    (HW_DIR / "entity_scenario_vs_hardware.json").write_text(json.dumps(out_json, indent=2))
+
+    # ---- CSV
+    fields = list(table[0].keys())
+    with (HW_DIR / "entity_scenario_vs_hardware.csv").open("w", newline="") as fh:
+        w = csv.DictWriter(fh, fieldnames=fields)
+        w.writeheader()
+        w.writerows(table)
+
+    # ---- human-readable text
+    write_text(summary, table)
+
+    # ---- figure
+    try:
+        write_figure(legend, data, hw, baseline_target, stressed_target, conn)
+    except Exception as exc:  # pragma: no cover - plotting is best-effort
+        print(f"[warn] figure skipped: {exc}")
+
+    print(f"Wrote entity_scenario_vs_hardware.{{csv,json,txt,png}} to {HW_DIR}")
+    print(f"  dataset: {summary['n_samples_dataset']} samples x {n} entities, "
+          f"{len(GENERATORS)} generators (BASELINE spec)")
+    print(f"  hardware: {summary['hw_n_unique_bitstrings']} unique bitstrings "
+          f"(STRESSED spec, ibm_boston)")
+    ebm = similarity["entangled_born_machine"]
+    print(f"  EBM vs HW structure: corr-Spearman {ebm['corr_offdiag_spearman_vs_hw']:+.2f}, "
+          f"connectivity-Spearman {ebm['connectivity_spearman_vs_hw']:+.2f}")
+
+
+def write_text(summary: dict, table: list[dict]) -> None:
+    L = []
+    L.append("PER-ENTITY: COMMITTED SCENARIO DATASET vs HARDWARE JOINT (ibm_boston)")
+    L.append("=" * 70)
+    reg = summary["regimes"]
+    L.append(f"Entities: {summary['n_entities']} named real G-SIBs/corporates "
+             f"(one per qubit).")
+    L.append(f"Dataset : {summary['n_samples_dataset']} samples/generator on the "
+             f"BASELINE spec (mean PD {reg['baseline_mean_pd']:.4f}).")
+    L.append(f"Hardware: {summary['hw_n_unique_bitstrings']} unique bitstrings on the "
+             f"STRESSED spec")
+    L.append(f"          (stressed target mean PD {reg['stressed_target_mean_pd']:.3f}, "
+             f"observed {reg['hw_observed_mean_pd']:.3f} -- decohered up toward 0.5).")
+    L.append("")
+    L.append("MAGNITUDES ARE REGIME-MISMATCHED (baseline vs stressed); compare STRUCTURE.")
+    L.append("")
+    L.append("GENERATOR vs HARDWARE -- cross-entity structure agreement")
+    L.append("-" * 70)
+    L.append(f"{'source':<22} {'marg-Spear':>11} {'corr-Spear':>11} "
+             f"{'corr-RMSE':>10} {'conn-Spear':>11}")
+    for gen, s in summary["generator_vs_hardware_structure"].items():
+        marg = s.get("marginal_spearman_vs_hw")
+        marg_s = f"{marg:>+11.3f}" if marg is not None else f"{'--':>11}"
+        L.append(f"{gen:<22} {marg_s} "
+                 f"{s['corr_offdiag_spearman_vs_hw']:>+11.3f} "
+                 f"{s['corr_offdiag_rmse_vs_hw']:>10.3f} "
+                 f"{s['connectivity_spearman_vs_hw']:>+11.3f}")
+    n_const = summary["generator_vs_hardware_structure"]["entangled_born_machine"].get(
+        "n_constant_cols")
+    L.append("")
+    L.append(f"Rare-event wall: at ~0.2% PDs a co-default is essentially never observed in "
+             f"{summary['n_samples_dataset']} samples,")
+    L.append(f"so empirical pairwise correlations are noise (and {n_const}/"
+             f"{summary['n_entities']} entities never default at all -> undefined, set 0).")
+    L.append("The 'baseline_target_spec' row uses the spec's INTENDED correlation instead "
+             "-- the honest")
+    L.append("structural reference at baseline PDs. This is exactly what motivates stressing "
+             "the spec.")
+    L.append("")
+    L.append("WITHIN-REGIME marginal rank fidelity (each source vs its OWN target)")
+    L.append("-" * 70)
+    for k, v in summary["within_regime_marginal_rank_fidelity"].items():
+        L.append(f"  {k:<32} Spearman {v:>+.3f}")
+    L.append("")
+    L.append("TOP-10 ENTITIES BY HARDWARE-OBSERVED CONNECTIVITY (mean |corr|)")
+    L.append("-" * 70)
+    L.append(f"{'name':<34}{'clu':>4}{'hw|corr|':>10}{'ebm|corr|':>11}"
+             f"{'hw_pd':>8}{'ebm_pd':>9}")
+    for r in sorted(table, key=lambda x: -x["hw_mean_abs_corr"])[:10]:
+        L.append(f"{r['name'][:33]:<34}{r['cluster']:>4}"
+                 f"{r['hw_mean_abs_corr']:>10.3f}{r['ebm_mean_abs_corr']:>11.3f}"
+                 f"{r['hw_observed_pd']:>8.3f}{r['ebm_pd']:>9.4f}")
+    L.append("")
+    (HW_DIR / "entity_scenario_vs_hardware.txt").write_text("\n".join(L) + "\n")
+
+
+def write_figure(legend, data, hw, baseline_target, stressed_target, conn) -> None:
+    import matplotlib
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    n = len(legend)
+    names = [r["ticker"] for r in legend]
+    clusters = np.array([r["cluster"] for r in legend])
+    cluster_colors = np.array(["#1f77b4", "#ff7f0e", "#2ca02c"])
+    x = np.arange(n)
+
+    fig, axes = plt.subplots(3, 1, figsize=(16, 13))
+
+    # (1) baseline-regime marginals: dataset generators + baseline target
+    ax = axes[0]
+    ax.plot(x, baseline_target, "k--", lw=1.5, label="baseline target", zorder=5)
+    for gen, mark in zip(["bernoulli", "gaussian_copula", "student_t_copula",
+                          "entangled_born_machine"], ["o", "s", "^", "D"]):
+        ax.plot(x, data[gen]["marginals"], mark, ms=4, alpha=0.7, label=gen)
+    ax.set_title("Baseline regime -- per-entity marginal PD (committed dataset, "
+                 "4000 samples/generator)")
+    ax.set_ylabel("default prob")
+    ax.set_xticks(x); ax.set_xticklabels(names, rotation=90, fontsize=6)
+    ax.legend(fontsize=7, ncol=5); ax.grid(alpha=0.3)
+
+    # (2) stressed/hardware-regime marginals
+    ax = axes[1]
+    ax.plot(x, stressed_target, "k--", lw=1.5, label="stressed loaded target")
+    ax.bar(x, hw["marginals"], color=cluster_colors[clusters], alpha=0.8,
+           label="hardware observed (ibm_boston)")
+    ax.axhline(0.5, color="grey", ls=":", lw=1, label="0.5 (max decoherence)")
+    ax.set_title("Stressed regime -- per-entity marginal PD (hardware joint vs loaded "
+                 "target); bar colour = cluster")
+    ax.set_ylabel("default prob")
+    ax.set_xticks(x); ax.set_xticklabels(names, rotation=90, fontsize=6)
+    ax.legend(fontsize=7); ax.grid(alpha=0.3)
+
+    # (3) structure: per-entity connectivity, EBM (baseline) vs hardware (stressed)
+    ax = axes[2]
+    ax.plot(x, conn["entangled_born_machine"], "D-", ms=4, color="#9467bd",
+            label="EBM mean |corr| (baseline)")
+    ax.plot(x, conn["hw"], "o-", ms=4, color="#d62728",
+            label="hardware mean |corr| (stressed)")
+    sp = spearman(conn["entangled_born_machine"], conn["hw"])
+    ax.set_title(f"Structure -- per-entity connectivity (mean |corr| to others); "
+                 f"EBM vs hardware rank Spearman {sp:+.2f}")
+    ax.set_ylabel("mean |corr|")
+    ax.set_xticks(x); ax.set_xticklabels(names, rotation=90, fontsize=6)
+    ax.legend(fontsize=7); ax.grid(alpha=0.3)
+
+    fig.tight_layout()
+    fig.savefig(HW_DIR / "entity_scenario_vs_hardware.png", dpi=110)
+    plt.close(fig)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/analyze_real_cluster_mixture_stress_hw.py b/scripts/analyze_real_cluster_mixture_stress_hw.py
new file mode 100644
index 0000000..fb57508
--- /dev/null
+++ b/scripts/analyze_real_cluster_mixture_stress_hw.py
@@ -0,0 +1,551 @@
+"""Comparative error/fidelity analysis: did the STRESS regime make the REAL 38-entity
+network measurable on hardware?
+
+Consumes three already-completed runs (NO QPU time, NO network here) on the SAME 3-cluster
+partition (14/14/10), 200k shots/cluster, ibm_boston:
+
+  1. BASELINE-HW  outputs/real_cluster_mixture_hw/            (real ~0.2% PDs, all below the
+                                                               ~2.7% device noise floor)
+  2. STRESS sim-preview  outputs/real_cluster_mixture_stress/ (exact statevector, 2008-calibrated
+                                                               ~15% mean PDs -- the faithful target)
+  3. STRESS-HW    outputs/real_cluster_mixture_stress_hw/     (same stressed spec on ibm_boston)
+
+It answers, quantitatively and bluntly:
+
+  1. ERROR/FIDELITY of stressed-HW vs its loaded target (per-cluster + overall marginal RMSE/TV,
+     within-cluster |corr| recovery, cross-cluster corr recovery, default-count TV), and the
+     decoherence bias (upward pull of marginals toward 0.5) related to circuit depth / 2q-gate
+     count.
+  2. THE KEY BEFORE/AFTER: stressed-HW vs BASELINE-HW. Does lifting the signal above the noise
+     floor recover the ORDERING of marginals (Spearman rank corr vs target, per cluster +
+     global, for BOTH runs) even when magnitudes stay biased? Correlations and downstream
+     cascade tail risk, before vs after.
+  3. GROUND-TRUTH framing: stressed-HW vs sim-preview vs reference on cascade tail risk
+     (P(severe)/CVaR/mean cascade count). Where does hardware land vs the faithful simulation?
+
+Reuses the repo's metrics (``_binary_corr``/``_mean_cross_block``/``default_count_distribution``/
+``total_variation``) and the diagnostics already serialized in the run reports + the reconciled
+global sample NPZs. Writes analysis_stress_comparison.{json,txt,png} into the stressed-HW dir.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+import sys
+
+import numpy as np
+from scipy.stats import spearmanr
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "src"))
+
+from systemic_risk.mixture.pipeline import default_count_distribution, total_variation
+from systemic_risk.mixture.reconcile import _binary_corr, _mean_cross_block
+
+BASE_DIR = ROOT / "outputs" / "real_cluster_mixture_hw"
+SIM_DIR = ROOT / "outputs" / "real_cluster_mixture_stress"
+STRESS_DIR = ROOT / "outputs" / "real_cluster_mixture_stress_hw"
+NOISE_FLOOR = 0.027
+
+
+def within_abs_corr(samples: np.ndarray) -> float:
+    if samples.shape[1] < 2:
+        return float("nan")
+    corr = np.corrcoef(samples, rowvar=False)
+    iu = np.triu_indices(corr.shape[0], k=1)
+    return float(np.nanmean(np.abs(corr[iu])))
+
+
+def per_cluster_fidelity(report: dict, run_tag: str) -> list[dict]:
+    """Per-cluster marginal RMSE / TV / Spearman-rank / |corr| recovery / decoherence mix-pull
+    of the observed hardware marginals against the loaded target, straight from the report's
+    per-cluster ``observed_marginals`` / ``target_marginals``.
+    """
+    rows = []
+    for hw in report["hardware_per_cluster"]:
+        obs = np.asarray(hw["observed_marginals"], dtype=float)
+        tgt = np.asarray(hw["target_marginals"], dtype=float)
+        tgt_corr = float(hw["target_within_abs_corr"])
+        obs_corr = float(hw["observed_within_abs_corr"])
+        # Spearman rank correlation of observed vs target marginals -- is the ORDERING recovered?
+        if np.ptp(tgt) > 0 and np.ptp(obs) > 0:
+            rho = float(spearmanr(obs, tgt).statistic)
+        else:
+            rho = float("nan")
+        rows.append({
+            "run": run_tag,
+            "cluster": hw["cluster"],
+            "qubits": len(hw["members"]),
+            "transpiled_depth": hw["circuit_depth"],
+            "transpiled_2q_gates": hw["two_qubit_gates"],
+            "target_marg_mean": round(float(tgt.mean()), 5),
+            "target_marg_min": round(float(tgt.min()), 5),
+            "target_marg_max": round(float(tgt.max()), 5),
+            "all_target_below_noise_floor": bool(np.all(tgt < NOISE_FLOOR)),
+            "n_target_above_floor": int(np.sum(tgt >= NOISE_FLOOR)),
+            "obs_marg_mean": round(float(obs.mean()), 5),
+            "marginal_rmse_vs_target": round(float(np.sqrt(np.mean((obs - tgt) ** 2))), 5),
+            "marginal_tv_vs_target": round(float(np.mean(np.abs(obs - tgt))), 5),
+            "marginal_spearman_vs_target": round(rho, 4),
+            "target_within_abs_corr": round(tgt_corr, 5),
+            "obs_within_abs_corr": round(obs_corr, 5),
+            "within_corr_recovery_fraction": (
+                round(obs_corr / tgt_corr, 4) if tgt_corr > 0 else float("nan")
+            ),
+            # decoherence: how far the device pulled marginals toward the 0.5 max-mixed state.
+            # 0 = pure (0/1), 0.5 = fully mixed. Reported as the mean over the cluster.
+            "marginal_mix_pull": round(float(np.mean(0.5 - np.abs(obs - 0.5))), 5),
+            # signed marginal bias (obs - target): positive = upward pull toward 0.5.
+            "marginal_signed_bias": round(float(np.mean(obs - tgt)), 5),
+        })
+    return rows
+
+
+def overall_marginal(report: dict) -> dict:
+    obs = np.concatenate([np.asarray(h["observed_marginals"], float)
+                          for h in report["hardware_per_cluster"]])
+    tgt = np.concatenate([np.asarray(h["target_marginals"], float)
+                          for h in report["hardware_per_cluster"]])
+    rho = float(spearmanr(obs, tgt).statistic)
+    return {
+        "marginal_rmse_vs_target": round(float(np.sqrt(np.mean((obs - tgt) ** 2))), 5),
+        "marginal_tv_vs_target": round(float(np.mean(np.abs(obs - tgt))), 5),
+        "marginal_spearman_vs_target_global": round(rho, 4),
+        "mean_signed_bias": round(float(np.mean(obs - tgt)), 5),
+        "target_mean": round(float(tgt.mean()), 5),
+        "obs_mean": round(float(obs.mean()), 5),
+    }
+
+
+def count_tv_between(a: np.ndarray, b: np.ndarray, n: int) -> float:
+    return total_variation(default_count_distribution(a, n),
+                           default_count_distribution(b, n))
+
+
+def main() -> None:
+    base_report = json.loads((BASE_DIR / "real_hardware_run_report.json").read_text())
+    stress_report = json.loads((STRESS_DIR / "real_stress_hardware_run_report.json").read_text())
+    sim_report = json.loads((SIM_DIR / "stress_preview_report.json").read_text())
+
+    n = int(stress_report["n"])
+    labels = np.asarray(stress_report["labels"], dtype=int)
+
+    # reconciled global sample matrices (reconciled / independent / reference) for each run
+    stress_npz = np.load(STRESS_DIR / "reconciled_global_stress.npz")
+    sim_npz = np.load(SIM_DIR / "reconciled_global_stress.npz")
+
+    # =====================================================================
+    # 1. STRESS-HW ERROR / FIDELITY vs its loaded target
+    # =====================================================================
+    stress_pc = per_cluster_fidelity(stress_report, "stress_hw")
+    stress_overall = overall_marginal(stress_report)
+    stress_overall["mean_within_corr_recovery_fraction"] = round(float(np.nanmean(
+        [r["within_corr_recovery_fraction"] for r in stress_pc])), 4)
+    # cross-cluster correlation recovery: achieved/target from the reconciled global samples
+    stress_cross_obs = _mean_cross_block(_binary_corr(stress_npz["reconciled"]), labels)
+    stress_cross_ref = _mean_cross_block(_binary_corr(stress_npz["reference"]), labels)
+    stress_overall["cross_cluster_corr_reconciled"] = round(float(stress_cross_obs), 5)
+    stress_overall["cross_cluster_corr_reference"] = round(float(stress_cross_ref), 5)
+    stress_overall["cross_cluster_corr_recovery_fraction"] = round(
+        float(stress_cross_obs / stress_cross_ref), 4)
+    # default-count TV of stress-HW reconciled vs its faithful sim-preview & vs reference
+    stress_overall["count_tv_hw_vs_simpreview"] = round(
+        count_tv_between(stress_npz["reconciled"], sim_npz["reconciled"], n), 5)
+    stress_overall["count_tv_hw_vs_reference"] = round(
+        count_tv_between(stress_npz["reconciled"], stress_npz["reference"], n), 5)
+
+    # =====================================================================
+    # 2. KEY BEFORE/AFTER: stress-HW vs BASELINE-HW
+    # =====================================================================
+    base_pc = per_cluster_fidelity(base_report, "baseline_hw")
+    base_overall = overall_marginal(base_report)
+    base_overall["mean_within_corr_recovery_fraction"] = round(float(np.nanmean(
+        [r["within_corr_recovery_fraction"] for r in base_pc])), 4)
+    base_cross_obs = base_report["diagnostics"]["reconciled"]["cross_cluster_corr"]
+    base_cross_ref = base_report["diagnostics"]["reference"]["cross_cluster_corr"]
+    base_overall["cross_cluster_corr_reconciled"] = round(float(base_cross_obs), 5)
+    base_overall["cross_cluster_corr_reference"] = round(float(base_cross_ref), 5)
+    base_overall["cross_cluster_corr_recovery_fraction"] = round(
+        float(base_cross_obs / base_cross_ref), 4)
+
+    before_after = {
+        "marginal_rmse_vs_target": {
+            "baseline_hw": base_overall["marginal_rmse_vs_target"],
+            "stress_hw": stress_overall["marginal_rmse_vs_target"],
+        },
+        "marginal_spearman_vs_target_global": {
+            "baseline_hw": base_overall["marginal_spearman_vs_target_global"],
+            "stress_hw": stress_overall["marginal_spearman_vs_target_global"],
+        },
+        "marginal_spearman_per_cluster": {
+            "baseline_hw": [r["marginal_spearman_vs_target"] for r in base_pc],
+            "stress_hw": [r["marginal_spearman_vs_target"] for r in stress_pc],
+        },
+        "mean_within_corr_recovery_fraction": {
+            "baseline_hw": base_overall["mean_within_corr_recovery_fraction"],
+            "stress_hw": stress_overall["mean_within_corr_recovery_fraction"],
+        },
+        "within_corr_recovery_per_cluster": {
+            "baseline_hw": [r["within_corr_recovery_fraction"] for r in base_pc],
+            "stress_hw": [r["within_corr_recovery_fraction"] for r in stress_pc],
+        },
+        "cross_cluster_corr_recovery_fraction": {
+            "baseline_hw": base_overall["cross_cluster_corr_recovery_fraction"],
+            "stress_hw": stress_overall["cross_cluster_corr_recovery_fraction"],
+        },
+    }
+
+    # downstream cascade tail risk, before/after, from each report's own diagnostics block
+    def tail(report: dict) -> dict:
+        d = report["diagnostics"]
+        sev = report.get("cascade_severe", {})
+        return {
+            "p_severe_reference": round(float(sev.get("reference", {}).get("p_severe", float("nan"))), 5),
+            "p_severe_reconciled": round(float(sev.get("reconciled", {}).get("p_severe", float("nan"))), 5),
+            "p_severe_independent": round(float(sev.get("independent", {}).get("p_severe", float("nan"))), 5),
+            "cvar_reference": round(float(d["reference"]["cascade_count_cvar"]), 4),
+            "cvar_reconciled": round(float(d["reconciled"]["cascade_count_cvar"]), 4),
+            "cvar_independent": round(float(d["independent"]["cascade_count_cvar"]), 4),
+            "mean_cascade_reference": round(float(d["reference"]["mean_cascade_count"]), 4),
+            "mean_cascade_reconciled": round(float(d["reconciled"]["mean_cascade_count"]), 4),
+            "mean_cascade_independent": round(float(d["independent"]["mean_cascade_count"]), 4),
+            "count_tv_reconciled_vs_ref": round(float(d["reconciled"]["count_tv_vs_ref"]), 5),
+        }
+
+    cascade_before_after = {
+        "baseline_hw": tail(base_report),
+        "stress_hw": tail(stress_report),
+        "sim_preview": tail(sim_report),
+    }
+
+    # =====================================================================
+    # 3. GROUND-TRUTH framing: stress-HW vs sim-preview vs reference
+    # =====================================================================
+    sd = stress_report["diagnostics"]
+    simd = sim_report["diagnostics"]
+    ground_truth = {
+        "note": ("reference = full-network Gaussian-copula joint on the STRESSED target; "
+                 "sim_preview = exact statevector (faithful); stress_hw = ibm_boston."),
+        "p_severe": {
+            "reference": round(float(stress_report["cascade_severe"]["reference"]["p_severe"]), 5),
+            "sim_preview": round(float(sim_report["cascade_severe"]["reconciled"]["p_severe"]), 5),
+            "stress_hw": round(float(stress_report["cascade_severe"]["reconciled"]["p_severe"]), 5),
+        },
+        "cascade_cvar95": {
+            "reference": round(float(sd["reference"]["cascade_count_cvar"]), 4),
+            "sim_preview": round(float(simd["reconciled"]["cascade_count_cvar"]), 4),
+            "stress_hw": round(float(sd["reconciled"]["cascade_count_cvar"]), 4),
+        },
+        "mean_cascade_count": {
+            "reference": round(float(sd["reference"]["mean_cascade_count"]), 4),
+            "sim_preview": round(float(simd["reconciled"]["mean_cascade_count"]), 4),
+            "stress_hw": round(float(sd["reconciled"]["mean_cascade_count"]), 4),
+        },
+        "marginal_rmse_vs_ref": {
+            "sim_preview": round(float(simd["reconciled"]["marginal_rmse_vs_ref"]), 5),
+            "stress_hw": round(float(sd["reconciled"]["marginal_rmse_vs_ref"]), 5),
+        },
+        "count_tv_vs_ref": {
+            "sim_preview": round(float(simd["reconciled"]["count_tv_vs_ref"]), 5),
+            "stress_hw": round(float(sd["reconciled"]["count_tv_vs_ref"]), 5),
+        },
+        "stress_hw_vs_simpreview_count_tv": stress_overall["count_tv_hw_vs_simpreview"],
+    }
+
+    analysis = {
+        "title": "Stress-regime hardware comparative analysis (REAL 38-entity network)",
+        "backend": stress_report["backend"],
+        "partition": {"cluster_sizes": stress_report["cluster_sizes"], "n": n},
+        "noise_floor": NOISE_FLOOR,
+        "regimes": {
+            "baseline_hw": "real PDs (~0.2% mean), all 38 below 2.7% floor",
+            "stress_hw": "2008-calibrated PDs (~15% mean), all 38 above floor",
+            "sim_preview": "exact statevector on the stressed spec (faithful target)",
+        },
+        "1_stress_hw_fidelity": {
+            "per_cluster": stress_pc,
+            "overall": stress_overall,
+        },
+        "2_before_after_baseline_vs_stress": {
+            "baseline_per_cluster": base_pc,
+            "stress_per_cluster": stress_pc,
+            "summary": before_after,
+            "cascade_tail_risk": cascade_before_after,
+        },
+        "3_ground_truth_framing": ground_truth,
+    }
+    (STRESS_DIR / "analysis_stress_comparison.json").write_text(json.dumps(analysis, indent=2))
+
+    # =====================================================================
+    # written summary
+    # =====================================================================
+    def fmt_pc(rows):
+        out = []
+        for r in rows:
+            out.append(
+                f"   {r['cluster']}      {r['qubits']:>2}    {r['transpiled_2q_gates']:>4}   "
+                f"{r['transpiled_depth']:>4} | {r['target_marg_mean']:>7.4f}  "
+                f"{r['obs_marg_mean']:>7.4f}  {r['marginal_rmse_vs_target']:>6.3f} | "
+                f"{r['marginal_spearman_vs_target']:>6.2f} | "
+                f"{r['target_within_abs_corr']:>6.3f} {r['obs_within_abs_corr']:>6.3f}  "
+                f"{r['within_corr_recovery_fraction'] if r['within_corr_recovery_fraction']==r['within_corr_recovery_fraction'] else float('nan'):>5.0%}"
+            )
+        return "\n".join(out)
+
+    ba = before_after
+    ca = cascade_before_after
+    gt = ground_truth
+    lines = []
+    lines.append("REAL 38-ENTITY HARDWARE -- STRESS vs BASELINE COMPARATIVE ANALYSIS")
+    lines.append("=" * 68)
+    lines.append("Backend: ibm_boston | 200,000 shots/cluster | same 14/14/10 partition")
+    lines.append("BASELINE-HW: real ~0.2% PDs, all 38 below the 2.7% noise floor.")
+    lines.append("STRESS-HW:   2008-calibrated ~15% PDs, all 38 ABOVE the floor (SNR mean ~5.6).")
+    lines.append("SIM-PREVIEW: exact statevector on the stressed spec = the faithful target.")
+    lines.append("Artifacts: analysis_stress_comparison.json (all numbers), .png, this file.")
+    lines.append("")
+    lines.append("BOTTOM LINE")
+    lines.append("-----------")
+    lines.append(
+        "Lifting the signal above the noise floor turned the real network from UNMEASURABLE "
+        "into a\nPARTIAL, structurally-meaningful hardware demonstration -- but NOT a "
+        "magnitude-faithful one.")
+    lines.append(
+        f"What stress BOUGHT us: the marginal ORDERING moves from noise to a genuinely positive "
+        f"rank signal\n(global Spearman vs target "
+        f"{ba['marginal_spearman_vs_target_global']['baseline_hw']:+.2f} baseline -> "
+        f"{ba['marginal_spearman_vs_target_global']['stress_hw']:+.2f} stress), and the "
+        f"within-cluster |corr| recovery becomes\nREAL rather than spurious "
+        f"({ba['mean_within_corr_recovery_fraction']['stress_hw']:.0%} of target on above-floor "
+        f"targets vs the baseline's noise-manufactured\ncorrelations on ~0 targets). The "
+        f"cross-cluster coupling is reconstructed "
+        f"({ba['cross_cluster_corr_recovery_fraction']['stress_hw']:.0%} of reference) -- but that "
+        f"is the\nclassical reconciler, not the device.")
+    lines.append(
+        f"What it did NOT buy: faithful marginal MAGNITUDES. Decoherence still pulls every qubit "
+        f"toward\n0.5, so the overall marginal RMSE vs target stays large "
+        f"({stress_overall['marginal_rmse_vs_target']:.3f}, mean signed\nbias "
+        f"{stress_overall['mean_signed_bias']:+.3f} = upward pull). The cascade tail is therefore "
+        f"HARDWARE-PERTURBED\n(overstated), not ground truth.")
+    lines.append("")
+    lines.append("1. STRESS-HW ERROR/FIDELITY vs LOADED TARGET (decoherence vs depth)")
+    lines.append("-" * 66)
+    lines.append("cl qub 2q-gates depth | tgt_marg obs_marg  RMSE | Spear | tgt|c| obs|c| recov")
+    lines.append(fmt_pc(stress_pc))
+    lines.append(
+        f"overall: marginal RMSE {stress_overall['marginal_rmse_vs_target']:.3f}, "
+        f"TV {stress_overall['marginal_tv_vs_target']:.3f}, "
+        f"global Spearman {stress_overall['marginal_spearman_vs_target_global']:+.2f}, "
+        f"signed bias {stress_overall['mean_signed_bias']:+.3f}")
+    lines.append(
+        f"         within-corr recovery {stress_overall['mean_within_corr_recovery_fraction']:.0%}, "
+        f"cross-cluster corr recovery "
+        f"{stress_overall['cross_cluster_corr_recovery_fraction']:.0%} "
+        f"({stress_overall['cross_cluster_corr_reconciled']:.3f} vs ref "
+        f"{stress_overall['cross_cluster_corr_reference']:.3f})")
+    lines.append(
+        f"         default-count TV: HW vs sim-preview {stress_overall['count_tv_hw_vs_simpreview']:.3f}, "
+        f"HW vs reference {stress_overall['count_tv_hw_vs_reference']:.3f}")
+    lines.append(
+        "- Decoherence bias: mix-pull (0=pure,0.5=mixed) tracks depth -- "
+        + ", ".join(f"c{r['cluster']}={r['marginal_mix_pull']:.2f}@d{r['transpiled_depth']}"
+                    for r in stress_pc)
+        + ". The deepest 14q blocks (478/459 2q-gates) inflate most; "
+          "the 10q block (211 gates) leaks least.")
+    lines.append(
+        "- Even above the floor, marginals are biased UP toward 0.5 -- the magnitudes are NOT "
+        "trustworthy; the ORDER is.")
+    lines.append("")
+    lines.append("2. BEFORE/AFTER -- BASELINE-HW vs STRESS-HW (what above-floor signal bought)")
+    lines.append("-" * 66)
+    lines.append("metric                                   baseline-HW    stress-HW")
+    lines.append(
+        f"marginal RMSE vs target                    {ba['marginal_rmse_vs_target']['baseline_hw']:>7.3f}      "
+        f"{ba['marginal_rmse_vs_target']['stress_hw']:>7.3f}")
+    lines.append(
+        f"marginal Spearman vs target (GLOBAL)       {ba['marginal_spearman_vs_target_global']['baseline_hw']:>+7.2f}      "
+        f"{ba['marginal_spearman_vs_target_global']['stress_hw']:>+7.2f}")
+    lines.append(
+        f"  per-cluster Spearman [c0,c1,c2]   base {ba['marginal_spearman_per_cluster']['baseline_hw']}  "
+        f"stress {ba['marginal_spearman_per_cluster']['stress_hw']}")
+    lines.append(
+        f"within-cluster |corr| recovery (mean)      {ba['mean_within_corr_recovery_fraction']['baseline_hw']:>6.0%}       "
+        f"{ba['mean_within_corr_recovery_fraction']['stress_hw']:>6.0%}")
+    lines.append(
+        f"cross-cluster corr recovery                {ba['cross_cluster_corr_recovery_fraction']['baseline_hw']:>6.0%}       "
+        f"{ba['cross_cluster_corr_recovery_fraction']['stress_hw']:>6.0%}")
+    lines.append("")
+    lines.append("   Cascade tail risk (post-contagion count), reconciled global joint:")
+    lines.append("   (P(severe) is omitted here: baseline & stress use DIFFERENT references, so it")
+    lines.append("    is not comparable across the two runs -- see section 3 for stress-vs-truth.)")
+    lines.append("   metric                  baseline-HW   stress-HW   sim-preview")
+    lines.append(
+        f"   CVaR95 cascade count     {ca['baseline_hw']['cvar_reconciled']:>8.2f}   "
+        f"{ca['stress_hw']['cvar_reconciled']:>8.2f}   "
+        f"{ca['sim_preview']['cvar_reconciled']:>8.2f}")
+    lines.append(
+        f"   mean cascade count       {ca['baseline_hw']['mean_cascade_reconciled']:>8.2f}   "
+        f"{ca['stress_hw']['mean_cascade_reconciled']:>8.2f}   "
+        f"{ca['sim_preview']['mean_cascade_reconciled']:>8.2f}")
+    lines.append(
+        f"   count-TV vs (own) ref    {ca['baseline_hw']['count_tv_reconciled_vs_ref']:>8.3f}   "
+        f"{ca['stress_hw']['count_tv_reconciled_vs_ref']:>8.3f}   "
+        f"{ca['sim_preview']['count_tv_reconciled_vs_ref']:>8.3f}")
+    lines.append(
+        "- Marginal ordering: baseline-HW global Spearman is a weak +0.25 dominated by noise "
+        "(targets all\n  buried under the floor; per-cluster it is [0.16, 0.24, 0.42]); stress-HW "
+        "rises to +0.36 GLOBALLY and to\n  +0.84 on cluster 1 -- the device now sees WHICH "
+        "entities are riskier where the signal is strongest.\n  NOTE: cluster 0 actually "
+        "regresses (Spearman -0.42): recovery is real but UNEVEN, not uniform.")
+    lines.append(
+        "- Correlations: the baseline's 103% within-|corr| 'recovery' is SPURIOUS -- noise "
+        "manufacturing\n  correlation on ~0 targets (per-cluster up to 186%). Stress-HW's 71% is a "
+        "GENUINE retention of real,\n  above-floor entanglement structure. So the honest story is "
+        "spurious->real, not low->high.")
+    lines.append(
+        "- Cascade: baseline-HW's reconciled count law is TV 0.92 from its own reference (no "
+        "signal, ~ the\n  independent baseline); stress-HW closes that to TV 0.44 -- markedly "
+        "closer to the right distribution,\n  though still OVERSTATED by the marginal inflation "
+        "(see section 3).")
+    lines.append("")
+    lines.append("3. GROUND-TRUTH FRAMING -- stress-HW vs sim-preview vs reference (tail risk)")
+    lines.append("-" * 66)
+    lines.append("metric                  reference   sim-preview(faithful)   stress-HW")
+    lines.append(
+        f"P(severe)               {gt['p_severe']['reference']:>8.3f}   "
+        f"{gt['p_severe']['sim_preview']:>13.3f}        {gt['p_severe']['stress_hw']:>8.3f}")
+    lines.append(
+        f"CVaR95 cascade count    {gt['cascade_cvar95']['reference']:>8.2f}   "
+        f"{gt['cascade_cvar95']['sim_preview']:>13.2f}        {gt['cascade_cvar95']['stress_hw']:>8.2f}")
+    lines.append(
+        f"mean cascade count      {gt['mean_cascade_count']['reference']:>8.2f}   "
+        f"{gt['mean_cascade_count']['sim_preview']:>13.2f}        {gt['mean_cascade_count']['stress_hw']:>8.2f}")
+    lines.append(
+        f"marginal RMSE vs ref          --     {gt['marginal_rmse_vs_ref']['sim_preview']:>13.3f}        "
+        f"{gt['marginal_rmse_vs_ref']['stress_hw']:>8.3f}")
+    lines.append(
+        f"count-TV vs ref               --     {gt['count_tv_vs_ref']['sim_preview']:>13.3f}        "
+        f"{gt['count_tv_vs_ref']['stress_hw']:>8.3f}")
+    lines.append(
+        f"- Hardware lands ABOVE the faithful sim-preview on every tail metric: it overstates "
+        f"P(severe)\n  ({gt['p_severe']['stress_hw']:.2f} vs sim {gt['p_severe']['sim_preview']:.2f}, "
+        f"ref {gt['p_severe']['reference']:.2f}) and mean cascade "
+        f"({gt['mean_cascade_count']['stress_hw']:.1f} vs sim "
+        f"{gt['mean_cascade_count']['sim_preview']:.1f}). CVaR is\n  closer (saturates near the "
+        f"system size). The HW reconciled count-distribution sits TV "
+        f"{gt['stress_hw_vs_simpreview_count_tv']:.2f} from\n  the faithful sim-preview -- "
+        "same regime, hardware-perturbed.")
+    lines.append("")
+    lines.append("VERDICT")
+    lines.append("-------")
+    lines.append(
+        "YES -- the stress regime makes the real 38-entity network a LEGITIMATE, if qualified, "
+        "hardware\ndemonstration. Above the noise floor the QPU recovers a real (if uneven) "
+        "marginal ORDERING and\nGENUINELY retains within-cluster correlation (71% of target) "
+        "instead of the baseline's noise-manufactured\nspurious correlation -- and the cascade "
+        "count law moves from TV 0.92 (no signal) to TV 0.44 vs truth.\nThis is a real signal, "
+        "not decoherence -- the before/after vs the buried baseline is unambiguous.")
+    lines.append(
+        "CAVEATS: (1) marginal MAGNITUDES remain decoherence-biased upward toward 0.5 (RMSE "
+        f"~{stress_overall['marginal_rmse_vs_target']:.2f}),\nso absolute PDs are not "
+        "trustworthy; (2) the cascade tail numbers are therefore HARDWARE-PERTURBED and\n"
+        "overstate risk relative to the faithful sim-preview; (3) the cross-cluster correlation "
+        "is rebuilt by\nthe CLASSICAL reconciler (fit to the spec target), not measured on the "
+        "device. Honest framing:\na correlation/ordering-faithful hardware demo, magnitude- and "
+        "tail-perturbed.")
+    summary = "\n".join(lines)
+    (STRESS_DIR / "analysis_stress_comparison.txt").write_text(summary + "\n")
+
+    # =====================================================================
+    # plot
+    # =====================================================================
+    try:
+        import matplotlib
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+
+        fig, axes = plt.subplots(1, 3, figsize=(17, 4.8))
+
+        # (a) observed vs target marginals, baseline vs stress, with floor
+        ax = axes[0]
+        for rep, color, tag in ((base_report, "tab:gray", "baseline-HW"),
+                                (stress_report, "tab:blue", "stress-HW")):
+            obs = np.concatenate([np.asarray(h["observed_marginals"], float)
+                                  for h in rep["hardware_per_cluster"]])
+            tgt = np.concatenate([np.asarray(h["target_marginals"], float)
+                                  for h in rep["hardware_per_cluster"]])
+            ax.scatter(tgt, obs, s=16, alpha=0.6, color=color, label=tag)
+        ax.axhline(NOISE_FLOOR, color="red", ls="--", lw=1, label="noise floor 2.7%")
+        ax.axvline(NOISE_FLOOR, color="red", ls=":", lw=1)
+        lim = [0, 0.62]
+        ax.plot(lim, lim, color="black", lw=1, label="perfect (obs=target)")
+        ax.set_xlim(lim)
+        ax.set_ylim(0, 0.75)
+        ax.set_xlabel("target default probability")
+        ax.set_ylabel("observed (hardware) marginal")
+        ax.set_title("(a) Observed vs target marginals\nbaseline (buried) vs stress (above floor)")
+        ax.legend(fontsize=7)
+
+        # (b) before/after recovery bars: rank, within-corr, cross-corr
+        ax = axes[1]
+        cats = ["marg rank\n(|Spearman|)", "within-corr\nrecovery", "cross-corr\nrecovery"]
+        base_vals = [abs(base_overall["marginal_spearman_vs_target_global"]),
+                     base_overall["mean_within_corr_recovery_fraction"],
+                     base_overall["cross_cluster_corr_recovery_fraction"]]
+        stress_vals = [abs(stress_overall["marginal_spearman_vs_target_global"]),
+                       stress_overall["mean_within_corr_recovery_fraction"],
+                       stress_overall["cross_cluster_corr_recovery_fraction"]]
+        x = np.arange(len(cats))
+        w = 0.36
+        bbars = ax.bar(x - w / 2, base_vals, w, color="tab:gray", label="baseline-HW")
+        ax.bar(x + w / 2, stress_vals, w, color="tab:blue", label="stress-HW")
+        # mark the baseline within-corr bar as SPURIOUS (noise-manufactured on ~0 targets)
+        bbars[1].set_hatch("xx")
+        bbars[1].set_edgecolor("red")
+        ax.text(1 - w / 2, base_vals[1] + 0.07, "spurious\n(noise)", ha="center",
+                fontsize=6, color="red")
+        for i, (b, s) in enumerate(zip(base_vals, stress_vals)):
+            ax.text(i - w / 2, b + 0.02, f"{b:.2f}", ha="center", fontsize=8)
+            ax.text(i + w / 2, s + 0.02, f"{s:.2f}", ha="center", fontsize=8)
+        ax.set_xticks(x)
+        ax.set_xticklabels(cats, fontsize=9)
+        ax.set_ylabel("recovery fraction")
+        ax.set_title("(b) Structure recovery: BEFORE vs AFTER\n"
+                     "(baseline within-corr is spurious; stress is real)")
+        ax.legend(fontsize=8)
+
+        # (c) cascade tail (CVaR + mean) reference vs sim vs stress-HW vs baseline-HW
+        ax = axes[2]
+        keys = ["reference\n(truth)", "sim-preview\n(faithful)", "stress-HW", "baseline-HW"]
+        cvars = [gt["cascade_cvar95"]["reference"], gt["cascade_cvar95"]["sim_preview"],
+                 gt["cascade_cvar95"]["stress_hw"], ca["baseline_hw"]["cvar_reconciled"]]
+        means = [gt["mean_cascade_count"]["reference"], gt["mean_cascade_count"]["sim_preview"],
+                 gt["mean_cascade_count"]["stress_hw"], ca["baseline_hw"]["mean_cascade_reconciled"]]
+        x = np.arange(len(keys))
+        w = 0.36
+        ax.bar(x - w / 2, cvars, w, color="tab:purple", label="CVaR95")
+        ax.bar(x + w / 2, means, w, color="tab:orange", label="mean count")
+        for i, (c, m) in enumerate(zip(cvars, means)):
+            ax.text(i - w / 2, c + 0.4, f"{c:.1f}", ha="center", fontsize=8)
+            ax.text(i + w / 2, m + 0.4, f"{m:.1f}", ha="center", fontsize=8)
+        ax.axhline(gt["cascade_cvar95"]["reference"], color="tab:purple", ls="--", lw=0.8)
+        ax.set_xticks(x)
+        ax.set_xticklabels(keys, fontsize=9)
+        ax.set_ylabel("post-cascade default count")
+        ax.set_title("(c) Cascade tail risk\n(stress-HW perturbed but right regime)")
+        ax.legend(fontsize=8)
+
+        fig.tight_layout()
+        fig.savefig(STRESS_DIR / "analysis_stress_comparison.png", dpi=130)
+        plot_note = "analysis_stress_comparison.png written"
+    except Exception as exc:  # pragma: no cover
+        plot_note = f"plot skipped: {exc}"
+
+    print(summary)
+    print("\n" + plot_note)
+    print(f"Saved -> {STRESS_DIR / 'analysis_stress_comparison.json'}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/export_joint_counts.py b/scripts/export_joint_counts.py
new file mode 100644
index 0000000..a276370
--- /dev/null
+++ b/scripts/export_joint_counts.py
@@ -0,0 +1,120 @@
+"""Export the cross-cluster 38-bit joint default-scenario counts (CSV) and a histogram.
+
+The joint is the reconciled global sample from the stressed hardware run: each of the 200k
+shots is a 38-bit correlated default scenario (1 = institution defaults post-cascade input).
+NOTE: this joint is classically reconciled from the three per-cluster device samples, not a
+native 38-qubit device measurement -- see the run's analysis notes.
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from collections import Counter
+from pathlib import Path
+
+import numpy as np
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "src"))
+from systemic_risk.data_network import build_network_spec
+
+OUTDIR = ROOT / "outputs" / "real_cluster_mixture_stress_hw"
+
+g = np.load(OUTDIR / "reconciled_global_stress.npz")
+joint = g["reconciled"]            # (200000, 38) int {0,1}
+labels = g["labels"]               # cluster id per column
+n_shots, n = joint.shape
+
+# --- qubit legend: which bank/company each bit position represents ---
+# Bit order in every bitstring below is positional: leftmost char = qubit 0,
+# rightmost = qubit 37, matching the node order of build_network_spec().
+emp = build_network_spec().empirical
+BLOC = {0: "A:US/Canada banks", 1: "B:Europe/UK+LatAm", 2: "C:Japan+corporates"}
+legend = []
+for i, ticker in enumerate(emp.node_ids):
+    a = emp.node_attributes[ticker]
+    legend.append({
+        "qubit": i,
+        "ticker": ticker,
+        "name": a.get("name", ""),
+        "node_type": a.get("node_type", ""),
+        "business_type": a.get("business_type", ""),
+        "region": a.get("region", ""),
+        "cluster": int(labels[i]),
+        "bloc": BLOC.get(int(labels[i]), str(int(labels[i]))),
+    })
+
+legend_csv = OUTDIR / "qubit_legend.csv"
+with legend_csv.open("w") as fh:
+    fh.write("qubit,ticker,name,node_type,business_type,region,cluster,bloc\n")
+    for r in legend:
+        fh.write(f'{r["qubit"]},{r["ticker"]},"{r["name"]}",{r["node_type"]},'
+                 f'{r["business_type"]},{r["region"]},{r["cluster"]},{r["bloc"]}\n')
+(OUTDIR / "qubit_legend.json").write_text(json.dumps(legend, indent=2))
+
+# --- per-bitstring joint counts -> CSV ---
+keys = ["".join(map(str, row)) for row in joint]
+counts = Counter(keys)
+rows = counts.most_common()        # sorted by count desc
+csv_path = OUTDIR / "joint_counts.csv"
+ticker_order = " ".join(r["ticker"] for r in legend)
+with csv_path.open("w") as fh:
+    fh.write(f"# bit order (left->right, qubit 0->37): {ticker_order}\n")
+    fh.write("# see qubit_legend.csv for the bank/company behind each position\n")
+    fh.write("bitstring,count,probability,n_defaults\n")
+    for bs, ct in rows:
+        fh.write(f"{bs},{ct},{ct / n_shots:.8f},{bs.count('1')}\n")
+
+# --- histograms ---
+defaults_per_shot = joint.sum(axis=1)            # Hamming weight per scenario
+hist = np.bincount(defaults_per_shot, minlength=n + 1)
+
+fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+
+# (a) THE correlated-failure histogram: how many institutions fail together per scenario
+axes[0].bar(np.arange(n + 1), hist, color="#b3202c", width=0.9)
+mean_def = defaults_per_shot.mean()
+p_severe = (defaults_per_shot >= 19).mean()
+axes[0].axvline(mean_def, color="k", ls="--", lw=1,
+                label=f"mean = {mean_def:.1f} defaults")
+axes[0].axvline(19, color="#444", ls=":", lw=1.2,
+                label=f"severe ≥19  (P={p_severe:.2f})")
+axes[0].set_xlabel("number of institutions defaulting in a scenario (of 38)")
+axes[0].set_ylabel("shot count")
+axes[0].set_title("(a) Cross-cluster joint: correlated-failure distribution")
+axes[0].legend(frameon=False)
+
+# (b) top joint scenarios by count (literal joint counts)
+topN = 25
+top = rows[:topN]
+labels = [f"{bs.count('1')}d" for bs, _ in top]   # label by #defaults (38-bit string too long)
+axes[1].bar(range(topN), [ct for _, ct in top], color="#2c5fb3")
+axes[1].set_xticks(range(topN))
+axes[1].set_xticklabels(labels, rotation=90, fontsize=7)
+axes[1].set_xlabel(f"top {topN} joint scenarios (labelled by # defaults)")
+axes[1].set_ylabel("shot count")
+axes[1].set_title("(b) Most frequent individual 38-bit joint scenarios")
+
+fig.suptitle(
+    "Stressed real-network (2008 regime) — cross-cluster joint default scenarios, "
+    f"{n_shots:,} shots, {len(counts):,} distinct",
+    fontsize=12,
+)
+fig.tight_layout(rect=(0, 0.1, 1, 0.96))
+# bloc membership footnote so the figure documents what the qubits are
+bloc_members = {b: [r["ticker"] for r in legend if r["bloc"] == b] for b in sorted(BLOC.values())}
+foot = "   |   ".join(f"{b}: {', '.join(ts)}" for b, ts in bloc_members.items())
+fig.text(0.5, 0.02, foot, ha="center", va="bottom", fontsize=7.5, wrap=True)
+png_path = OUTDIR / "joint_counts_hist.png"
+fig.savefig(png_path, dpi=140)
+
+print(f"shots={n_shots:,}  distinct_joint_scenarios={len(counts):,}")
+print(f"mean defaults/scenario={mean_def:.2f}  P(severe>=19)={p_severe:.3f}  "
+      f"max defaults in any shot={defaults_per_shot.max()}")
+print(f"CSV  -> {csv_path}  ({len(rows):,} rows)")
+print(f"PNG  -> {png_path}")
diff --git a/scripts/run_real_cluster_mixture_stress.py b/scripts/run_real_cluster_mixture_stress.py
new file mode 100644
index 0000000..f0e0da2
--- /dev/null
+++ b/scripts/run_real_cluster_mixture_stress.py
@@ -0,0 +1,267 @@
+"""SIMULATION preview of the 3-cluster mixture over the STRESSED real exposure network.
+
+The no-hardware reference twin of ``scripts/run_real_cluster_mixture_hardware.py``. It applies
+the 2008-style stress transform (``systemic_risk.data_network.apply_stress``) to the real
+38-entity network, then runs the EXACT-STATEVECTOR cluster mixture on the EXISTING persisted
+3-cluster partition (``outputs/data_network/real_network_partition.json``, sizes 14/14/10 --
+reused, never re-derived):
+
+  1. stress the baseline spec -> crisis marginals (mean PD ~15%), correlation UNCHANGED;
+  2. confirm loadability: how many of the 38 stressed PDs clear the ~2.7% QPU noise floor;
+  3. sample each cluster INDEPENDENTLY via its own exact statevector loader
+     (``sample_clusters_statevector`` -- the simulated stand-in for one device per cluster);
+  4. reconcile the per-cluster samples into the global joint with the common-shock reconciler
+     fit to the stressed spec's own cross-cluster co-default target;
+  5. run the GLOBAL cascade on the real exposure graph and report the tail risk
+     (P(severe) / CVaR / mean cascade count) under stress.
+
+This is the faithful reference a hardware run on the stressed spec SHOULD reproduce. No QPU is
+touched -- compute only. Artifacts go to ``outputs/real_cluster_mixture_stress/``.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+import sys
+
+import numpy as np
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "src"))
+
+from systemic_risk.data_network import QPU_NOISE_FLOOR, apply_stress, build_network_spec
+from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator
+from systemic_risk.generators.quantum.budget_clustering import (
+    ClusterPartition,
+    _within_cut_weight,
+    dependency_for_clustering,
+)
+from systemic_risk.mixture import (
+    CommonShockReconciler,
+    cross_cluster_corr_target,
+    independent_global_samples,
+    reconciliation_diagnostics,
+    sample_clusters_statevector,
+)
+from systemic_risk.mixture.reconcile import _binary_corr, _mean_cross_block
+from systemic_risk.spec import SystemSpec
+
+DEFAULT_PARTITION = ROOT / "outputs" / "data_network" / "real_network_partition.json"
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument("--partition", type=Path, default=DEFAULT_PARTITION)
+    parser.add_argument("--target-mean-pd", type=float, default=0.15,
+                        help="Cross-entity mean stressed PD (2008 anchor; default 0.15).")
+    parser.add_argument("--crisis-floor", type=float, default=QPU_NOISE_FLOOR,
+                        help="Floor every stressed PD to this (default = noise floor 0.027) so "
+                             "all entities clear the readout floor; re-solved to hold the mean.")
+    parser.add_argument("--edge-threshold", type=float, default=0.02)
+    parser.add_argument("--n-samples", type=int, default=200_000,
+                        help="Samples per cluster / reference (exact statevector draws).")
+    parser.add_argument("--calib-iters", type=int, default=30)
+    parser.add_argument("--cascade-max-eval", type=int, default=4000)
+    parser.add_argument("--severe-fraction", type=float, default=0.5,
+                        help="P(severe) = P(post-cascade default count >= severe_fraction * n).")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument(
+        "--output-dir", type=Path, default=ROOT / "outputs" / "real_cluster_mixture_stress"
+    )
+    return parser.parse_args()
+
+
+def load_partition(path: Path, spec: SystemSpec) -> tuple[ClusterPartition, dict]:
+    """Rebuild the persisted partition as a ClusterPartition (clusters never re-derived)."""
+    record = json.loads(path.read_text(encoding="utf-8"))
+    clusters = [sorted(int(m) for m in c["member_indices"]) for c in record["clusters"]]
+    labels = np.asarray(record["labels"], dtype=int)
+    if labels.shape[0] != spec.n:
+        raise ValueError(f"partition labels ({labels.shape[0]}) != spec.n ({spec.n})")
+    dep = dependency_for_clustering(spec)
+    within, cut = _within_cut_weight(dep, labels)
+    partition = ClusterPartition(
+        clusters=clusters,
+        labels=labels,
+        max_cluster_size=max(len(c) for c in clusters),
+        within_weight=within,
+        cut_weight=cut,
+    )
+    return partition, record
+
+
+def cluster_targets(spec: SystemSpec, partition: ClusterPartition) -> list[dict]:
+    """Per-cluster target marginals + within-cluster correlation the loader must reproduce."""
+    p = np.asarray(spec.marginal_default_probs, dtype=float)
+    dep = np.abs(spec.dependency_matrix())
+    out = []
+    for idx, members in enumerate(partition.clusters):
+        pm = p[members]
+        sub = dep[np.ix_(members, members)]
+        iu = np.triu_indices(sub.shape[0], k=1)
+        out.append({
+            "cluster": idx,
+            "size": len(members),
+            "member_indices": list(members),
+            "member_names": [spec.node_names[m] for m in members],
+            "target_pd_min": round(float(pm.min()), 5),
+            "target_pd_max": round(float(pm.max()), 5),
+            "target_pd_mean": round(float(pm.mean()), 5),
+            "n_above_noise_floor": int(np.sum(pm >= QPU_NOISE_FLOOR)),
+            "target_within_abs_corr_mean": round(float(np.mean(sub[iu])), 4),
+        })
+    return out
+
+
+def loadability_report(base: SystemSpec, stressed: SystemSpec, calib) -> dict:
+    pb = np.asarray(base.marginal_default_probs, dtype=float)
+    ps = np.asarray(stressed.marginal_default_probs, dtype=float)
+    below = [i for i in range(stressed.n) if ps[i] < QPU_NOISE_FLOOR]
+    return {
+        "noise_floor": QPU_NOISE_FLOOR,
+        "baseline_mean_pd": round(float(pb.mean()), 6),
+        "baseline_n_above_floor": int(np.sum(pb >= QPU_NOISE_FLOOR)),
+        "stressed_mean_pd": round(float(ps.mean()), 6),
+        "stressed_min_pd": round(float(ps.min()), 6),
+        "stressed_max_pd": round(float(ps.max()), 6),
+        "stressed_n_above_floor": int(np.sum(ps >= QPU_NOISE_FLOOR)),
+        "n_entities": stressed.n,
+        "snr_min": round(float(ps.min() / QPU_NOISE_FLOOR), 3),
+        "snr_mean": round(float(ps.mean() / QPU_NOISE_FLOOR), 3),
+        "entities_below_floor": [
+            {"index": i, "name": stressed.node_names[i], "pd": round(float(ps[i]), 6)}
+            for i in below
+        ],
+        "all_clear_floor": len(below) == 0,
+        "calibration": calib.to_dict(),
+    }
+
+
+def severe_probabilities(
+    spec: SystemSpec, reference, reconciled, independent, severe_fraction: float, max_eval: int
+) -> dict:
+    """P(post-cascade default count >= severe_fraction * n) for each joint, on the real graph."""
+    from systemic_risk.mixture.pipeline import cascade_loss_cvar
+
+    threshold = int(np.ceil(severe_fraction * spec.n))
+
+    def p_severe(samples) -> dict:
+        _, counts = cascade_loss_cvar(samples, spec, alpha=0.95, max_eval=max_eval)
+        return {
+            "p_severe": round(float(np.mean(counts >= threshold)), 5),
+            "mean_cascade_count": round(float(counts.mean()), 4),
+        }
+
+    return {
+        "severe_threshold_count": threshold,
+        "severe_fraction": severe_fraction,
+        "reference": p_severe(reference),
+        "reconciled": p_severe(reconciled),
+        "independent": p_severe(independent),
+    }
+
+
+def main() -> None:
+    args = parse_args()
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    base = build_network_spec().to_system_spec()
+    stressed, calib = apply_stress(
+        base, target_mean_pd=args.target_mean_pd, crisis_floor=args.crisis_floor
+    )
+    partition, partition_record = load_partition(args.partition, stressed)
+
+    load_rep = loadability_report(base, stressed, calib)
+    targets = cluster_targets(stressed, partition)
+
+    # --- exact-statevector cluster mixture on the STRESSED spec ----------------------------
+    labels = partition.labels
+    clusters = sample_clusters_statevector(
+        stressed, partition, args.n_samples, seed=args.seed + 1,
+        edge_threshold=args.edge_threshold, calibration_iterations=args.calib_iters,
+    )
+    target_cross = cross_cluster_corr_target(stressed, labels)
+    rec = CommonShockReconciler(stressed.n, labels).fit_reconcile(
+        clusters, target_cross, args.n_samples, seed=args.seed + 2
+    )
+    independent = independent_global_samples(clusters, stressed.n)
+
+    ref_gen = GaussianCopulaGenerator()
+    ref_gen.fit(stressed)
+    reference = ref_gen.sample(args.n_samples, seed=args.seed + 3)
+
+    diag = reconciliation_diagnostics(
+        reference, rec.samples, independent, labels, stressed.n,
+        cascade_spec=stressed, cascade_max_eval=args.cascade_max_eval,
+    )
+    severe = severe_probabilities(
+        stressed, reference, rec.samples, independent,
+        args.severe_fraction, args.cascade_max_eval,
+    )
+
+    report = {
+        "status": "simulation-preview",
+        "system": "REAL 38-entity exposure network -- 2008 STRESS, exact statevector (no hardware)",
+        "n": stressed.n,
+        "partition_source": str(args.partition),
+        "partition_content_hash": partition_record.get("content_hash"),
+        "cluster_sizes": partition.sizes,
+        "labels": labels.tolist(),
+        "reference": "full-network Gaussian-copula joint fit to the STRESSED target correlation",
+        "n_samples": args.n_samples,
+        "loadability": load_rep,
+        "cluster_targets": targets,
+        "fitted_beta": round(rec.beta, 5),
+        "target_cross_corr": round(rec.target_cross_corr, 5),
+        "achieved_cross_corr": round(rec.achieved_cross_corr, 5),
+        "cascade_severe": severe,
+        "diagnostics": diag,
+    }
+
+    report_path = args.output_dir / "stress_preview_report.json"
+    report_path.write_text(json.dumps(report, indent=2), encoding="utf-8")
+    stressed.save_json(args.output_dir / "stressed_spec.json")
+    np.savez_compressed(
+        args.output_dir / "reconciled_global_stress.npz",
+        reconciled=rec.samples, independent=independent,
+        reference=reference, labels=labels,
+    )
+
+    print(json.dumps({
+        "status": report["status"],
+        "loadability": {
+            k: load_rep[k] for k in (
+                "stressed_mean_pd", "stressed_min_pd", "stressed_max_pd",
+                "stressed_n_above_floor", "n_entities", "snr_min", "all_clear_floor",
+            )
+        },
+        "cluster_targets": [
+            {k: t[k] for k in (
+                "cluster", "size", "target_pd_mean", "target_pd_min", "target_pd_max",
+                "target_within_abs_corr_mean",
+            )} for t in targets
+        ],
+        "fitted_beta": report["fitted_beta"],
+        "target_cross_corr": report["target_cross_corr"],
+        "achieved_cross_corr": report["achieved_cross_corr"],
+        "cascade_severe": severe,
+        "cascade_cvar": {
+            "reference": round(diag["reference"]["cascade_count_cvar"], 3),
+            "reconciled": round(diag["reconciled"]["cascade_count_cvar"], 3),
+            "independent": round(diag["independent"]["cascade_count_cvar"], 3),
+        },
+        "mean_cascade_count": {
+            "reference": round(diag["reference"]["mean_cascade_count"], 3),
+            "reconciled": round(diag["reconciled"]["mean_cascade_count"], 3),
+            "independent": round(diag["independent"]["mean_cascade_count"], 3),
+        },
+    }, indent=2))
+    print(f"\nSaved report -> {report_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/run_real_cluster_mixture_stress_hardware.py b/scripts/run_real_cluster_mixture_stress_hardware.py
new file mode 100644
index 0000000..e586641
--- /dev/null
+++ b/scripts/run_real_cluster_mixture_stress_hardware.py
@@ -0,0 +1,404 @@
+"""Run the 3-cluster mixture over the STRESSED real exposure network on IBM hardware.
+
+This is the HARDWARE twin of ``scripts/run_real_cluster_mixture_stress.py`` (the compute-only
+simulation preview) and the STRESSED analogue of ``scripts/run_real_cluster_mixture_hardware.py``
+(the baseline, noise-dominated hardware run). It:
+
+  1. builds the real 38-entity exposure-network spec,
+  2. applies the 2008-style stress transform
+     (``systemic_risk.data_network.apply_stress(spec, target_mean_pd=0.15,
+     crisis_floor=0.027)``) -- a rank-preserving logit-space shift that lifts the cross-entity
+     MEAN PD to ~15% (every entity above the ~2.7% QPU readout floor) while leaving the real
+     correlation graph UNCHANGED,
+  3. reuses the EXISTING persisted 3-cluster partition
+     (``outputs/data_network/real_network_partition.json`` -- k=3, sizes 14/14/10; never
+     re-derived),
+  4. runs the SAME shape of experiment as the baseline hardware run: one logical QPU experiment
+     per cluster, each cluster's fitted dense (RY + CRY) block circuit submitted to a backend,
+     measured bitstrings reconciled locally into the global joint via the common-shock
+     reconciler, and that joint fed through the cascade on the real exposure graph.
+
+WHY STRESS, NOT FAITHFUL
+------------------------
+The baseline run (``run_real_cluster_mixture_hardware.py``) loaded the real ~0.2%-mean PDs
+faithfully: they sit BELOW the ~2.7% noise floor, so the marginals came back as pure noise
+(per-cluster marginal RMSE ~0.20-0.23, see ``outputs/real_cluster_mixture_hw/``). The stress
+transform lifts every PD above the floor (SNR_mean ~5.6 in the sim preview), so this run is the
+first that can plausibly recover the loaded marginals AND correlation on hardware. The faithful
+reference it should reproduce is ``outputs/real_cluster_mixture_stress/stress_preview_report.json``.
+
+HONESTY CAVEATS baked in:
+
+  * The stress overlay is a HYPOTHETICAL uniform crisis (one logit shift for all entities),
+    calibrated to historical aggregates (Moody's GFC default rates + FRED BAA-AAA spread ~3.85x),
+    not an entity-by-entity 2008 re-rating. Recorded in ``stress_calibration`` and the spec's
+    ``metadata['stress']``.
+  * These are DENSE blocks (every within-cluster edge above ``--edge-threshold``), well past the
+    documented coherence boundary, so decoherence still attacks the within-cluster correlation;
+    how much survives is exactly what this run measures.
+
+Always dry-run first (omit ``--submit``) to see per-cluster gate/qubit/entangler budget, the
+shot-batching plan, and the stressed-PD inspection (proving every entity clears the floor). With
+``--submit`` it spends metered QPU time: one logical experiment per cluster, shots auto-batched to
+the backend per-job cap and aggregated. Artifacts go to ``outputs/real_cluster_mixture_stress_hw/``
+(distinct from the baseline ``real_cluster_mixture_hw/`` and the sim preview
+``real_cluster_mixture_stress/``).
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+import sys
+
+import numpy as np
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "src"))
+
+from systemic_risk.data_network import QPU_NOISE_FLOOR, apply_stress, build_network_spec
+from systemic_risk.generators.gaussian_copula import GaussianCopulaGenerator
+from systemic_risk.generators.quantum import ansatz as A
+from systemic_risk.generators.quantum.ibm_runtime import _split_shots, run_block
+from systemic_risk.mixture import (
+    CommonShockReconciler,
+    cluster_samples_from_bitstrings,
+    cross_cluster_corr_target,
+    independent_global_samples,
+    reconciliation_diagnostics,
+)
+from systemic_risk.mixture.cluster_loader import _statevector_block_moments
+from systemic_risk.spec import SystemSpec
+
+DEFAULT_PARTITION = ROOT / "outputs" / "data_network" / "real_network_partition.json"
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument("--partition", type=Path, default=DEFAULT_PARTITION)
+    parser.add_argument("--target-mean-pd", type=float, default=0.15,
+                        help="Cross-entity mean stressed PD (2008 anchor; default 0.15).")
+    parser.add_argument("--crisis-floor", type=float, default=QPU_NOISE_FLOOR,
+                        help="Floor every stressed PD to this (default = noise floor 0.027) so "
+                             "all entities clear the readout floor; re-solved to hold the mean.")
+    parser.add_argument("--edge-threshold", type=float, default=0.02)
+    parser.add_argument("--calib-iters", type=int, default=30)
+    parser.add_argument("--backend", default="ibm_boston")
+    parser.add_argument("--shots", type=int, default=200_000,
+                        help="Total shots per cluster (auto-batched to backend cap).")
+    parser.add_argument("--max-shots-per-job", type=int, default=None,
+                        help="Override the backend per-job shot cap for batch planning "
+                             "(dry-run only; live runs read the real cap from the backend).")
+    parser.add_argument("--optimization-level", type=int, choices=range(4), default=3)
+    parser.add_argument("--submit", action="store_true", help="Submit the metered IBM jobs.")
+    parser.add_argument("--ref-samples", type=int, default=200_000,
+                        help="Gaussian-copula reference sample count for diagnostics.")
+    parser.add_argument("--cascade-max-eval", type=int, default=4000)
+    parser.add_argument("--severe-fraction", type=float, default=0.5,
+                        help="P(severe) = P(post-cascade default count >= severe_fraction * n).")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument(
+        "--output-dir", type=Path,
+        default=ROOT / "outputs" / "real_cluster_mixture_stress_hw"
+    )
+    return parser.parse_args()
+
+
+def load_partition(path: Path) -> tuple[list[list[int]], np.ndarray, dict]:
+    """Load the persisted real-network partition: cluster member-index lists + global labels."""
+    record = json.loads(path.read_text(encoding="utf-8"))
+    clusters = [sorted(int(m) for m in c["member_indices"]) for c in record["clusters"]]
+    labels = np.asarray(record["labels"], dtype=int)
+    return clusters, labels, record
+
+
+def build_block(spec: SystemSpec, members, *, threshold: float, calib_iters: int):
+    """Fitted dense block circuit for one cluster (all within-cluster edges above threshold).
+
+    Identical construction to ``run_real_cluster_mixture_hardware.build_block`` -- reused so the
+    stressed run is the same circuit shape as the baseline one (only the marginals differ).
+    """
+    members = sorted(int(m) for m in members)
+    p = np.clip(spec.marginal_default_probs, 1e-6, 1.0 - 1e-6)
+    cov = A.target_covariance(spec)
+    member_set = set(members)
+    edges = [
+        (i, j)
+        for (i, j) in A.dependency_edges(spec, threshold=threshold, within_clusters_only=False)
+        if i in member_set and j in member_set
+    ]
+    circuit = A._block_circuit(members, p, cov, edges)
+    if circuit.size > 1 and circuit.edges:
+        circuit = A.calibrate_block(circuit, _statevector_block_moments, iterations=calib_iters)
+    return circuit
+
+
+def within_abs_corr(samples: np.ndarray) -> float:
+    """Mean |pairwise correlation| inside a cluster's samples (collapses to 0 when decohered)."""
+    if samples.shape[1] < 2:
+        return float("nan")
+    corr = np.corrcoef(samples, rowvar=False)
+    iu = np.triu_indices(corr.shape[0], k=1)
+    return float(np.nanmean(np.abs(corr[iu])))
+
+
+def target_within_abs_corr(spec: SystemSpec, members) -> float:
+    dep = np.abs(spec.dependency_matrix())
+    sub = dep[np.ix_(members, members)]
+    iu = np.triu_indices(sub.shape[0], k=1)
+    return float(np.mean(sub[iu]))
+
+
+def shot_plan(shots: int, max_shots_per_job: int | None) -> dict:
+    """Describe how ``shots`` would batch into per-job publications for the dry-run report."""
+    if max_shots_per_job is None:
+        return {
+            "total_shots_per_cluster": shots,
+            "max_shots_per_job": "read from backend at submit time",
+            "note": "batches computed live from backend.configuration().max_shots",
+        }
+    batches = _split_shots(shots, max_shots_per_job)
+    return {
+        "total_shots_per_cluster": shots,
+        "max_shots_per_job": max_shots_per_job,
+        "n_jobs_per_cluster": len(batches),
+        "shot_batches": list(batches),
+    }
+
+
+def loadability_report(base: SystemSpec, stressed: SystemSpec) -> dict:
+    pb = np.asarray(base.marginal_default_probs, dtype=float)
+    ps = np.asarray(stressed.marginal_default_probs, dtype=float)
+    below = [i for i in range(stressed.n) if ps[i] < QPU_NOISE_FLOOR]
+    return {
+        "noise_floor": QPU_NOISE_FLOOR,
+        "baseline_mean_pd": round(float(pb.mean()), 6),
+        "baseline_n_above_floor": int(np.sum(pb >= QPU_NOISE_FLOOR)),
+        "stressed_mean_pd": round(float(ps.mean()), 6),
+        "stressed_min_pd": round(float(ps.min()), 6),
+        "stressed_max_pd": round(float(ps.max()), 6),
+        "stressed_n_above_floor": int(np.sum(ps >= QPU_NOISE_FLOOR)),
+        "n_entities": stressed.n,
+        "snr_min": round(float(ps.min() / QPU_NOISE_FLOOR), 3),
+        "snr_mean": round(float(ps.mean() / QPU_NOISE_FLOOR), 3),
+        "all_clear_floor": len(below) == 0,
+    }
+
+
+def pd_inspection(stressed: SystemSpec, clusters) -> list[dict]:
+    ps = np.asarray(stressed.marginal_default_probs, dtype=float)
+    out = []
+    for idx, members in enumerate(clusters):
+        pm = ps[members]
+        out.append({
+            "cluster": idx,
+            "members": members,
+            "pd_min": round(float(pm.min()), 6),
+            "pd_max": round(float(pm.max()), 6),
+            "pd_mean": round(float(pm.mean()), 6),
+            "n_above_noise_floor": int(np.sum(pm >= QPU_NOISE_FLOOR)),
+            "all_above_noise_floor": bool(np.all(pm >= QPU_NOISE_FLOOR)),
+        })
+    return out
+
+
+def main() -> None:
+    args = parse_args()
+    base_spec = build_network_spec().to_system_spec()
+    spec, calib = apply_stress(
+        base_spec, target_mean_pd=args.target_mean_pd, crisis_floor=args.crisis_floor
+    )
+    clusters, labels, partition_record = load_partition(args.partition)
+
+    if labels.shape[0] != spec.n:
+        raise ValueError(f"partition labels ({labels.shape[0]}) != spec.n ({spec.n})")
+
+    blocks = [
+        build_block(spec, members, threshold=args.edge_threshold, calib_iters=args.calib_iters)
+        for members in clusters
+    ]
+
+    plan = {
+        "system": "REAL 38-entity exposure network -- 2008 STRESS, DENSE blocks on hardware",
+        "n": spec.n,
+        "backend": args.backend,
+        "partition_source": str(args.partition),
+        "partition_content_hash": partition_record.get("content_hash"),
+        "cluster_sizes": [b.size for b in blocks],
+        "labels": labels.tolist(),
+        "stress_calibration": calib.to_dict(),
+        "loadability": loadability_report(base_spec, spec),
+        "noise_floor_assumed": QPU_NOISE_FLOOR,
+        "pd_inspection": pd_inspection(spec, clusters),
+        "shot_plan": shot_plan(args.shots, args.max_shots_per_job),
+        "per_cluster": [
+            {
+                "cluster": idx,
+                "members": list(b.qubits),
+                "qubits": b.size,
+                "entanglers": len(b.edges),
+                "approx_two_qubit_gates_pretranspile": 2 * len(b.edges),
+                "entanglement_depth_pretranspile": b.entanglement_depth,
+                "target_within_abs_corr": round(target_within_abs_corr(spec, list(b.qubits)), 4),
+                "target_pd_mean": round(
+                    float(np.asarray(spec.marginal_default_probs)[list(b.qubits)].mean()), 4
+                ),
+            }
+            for idx, b in enumerate(blocks)
+        ],
+    }
+
+    if not args.submit:
+        print(json.dumps({
+            "status": "dry-run",
+            **plan,
+            "note": ("STRESSED marginals (mean PD ~15%) all clear the ~2.7% noise floor, so "
+                     "marginals should now be recoverable (unlike the baseline faithful run). "
+                     "Dense blocks still past the coherence boundary -- correlation recovery is "
+                     "what this measures. Add --submit to spend metered QPU."),
+            "next_command": (
+                "uv run --extra quantum python "
+                "scripts/run_real_cluster_mixture_stress_hardware.py --submit"
+            ),
+        }, indent=2))
+        return
+
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    # Persist the resolved plan + stressed spec up front for resumability.
+    (args.output_dir / "plan.json").write_text(json.dumps(plan, indent=2), encoding="utf-8")
+    spec.save_json(args.output_dir / "stressed_spec.json")
+    live_jobs: list[dict] = []
+    live_path = args.output_dir / "job_ids_live.json"
+
+    cluster_samples = []
+    hw_reports = []
+    p_stressed = np.asarray(spec.marginal_default_probs, dtype=float)
+    for idx, block in enumerate(blocks):
+        result = run_block(
+            block,
+            shots=args.shots,
+            backend_name=args.backend,
+            optimization_level=args.optimization_level,
+        )
+        members = list(block.qubits)
+        # Persist job id immediately for resumability before doing any post-processing.
+        live_jobs.append({
+            "cluster": idx,
+            "job_id": result.job_id,
+            "backend": result.backend_name,
+            "shots": result.shots,
+            "shot_batches": list(result.shot_batches),
+        })
+        live_path.write_text(json.dumps({
+            "run": "200k-shot STRESSED real-network 3-cluster mixture on hardware",
+            "backend": args.backend,
+            "shots_per_cluster": args.shots,
+            "jobs": live_jobs,
+            "note": "job ids persisted incrementally for resumability",
+        }, indent=2), encoding="utf-8")
+
+        npz_path = args.output_dir / f"cluster{idx}_{result.backend_name}_{result.job_id}.npz"
+        np.savez_compressed(npz_path, samples=result.samples, members=np.asarray(members))
+        cluster_samples.append(
+            cluster_samples_from_bitstrings(
+                members, result.samples, source=f"hardware:{result.backend_name}"
+            )
+        )
+        observed = result.samples.mean(axis=0)
+        target = p_stressed[members]
+        hw_reports.append({
+            "cluster": idx,
+            "members": members,
+            "backend": result.backend_name,
+            "job_id": result.job_id,
+            "shots": result.shots,
+            "shot_batches": list(result.shot_batches),
+            "n_jobs": len(result.shot_batches),
+            "circuit_depth": result.circuit_depth,
+            "two_qubit_gates": result.two_qubit_gates,
+            "circuit_operations": result.circuit_operations,
+            "marginal_rmse_vs_target": round(
+                float(np.sqrt(np.mean((observed - target) ** 2))), 5
+            ),
+            "observed_marginals": [round(float(x), 5) for x in observed],
+            "target_marginals": [round(float(x), 5) for x in target],
+            "target_within_abs_corr": round(target_within_abs_corr(spec, members), 4),
+            "observed_within_abs_corr": round(within_abs_corr(result.samples), 4),
+            "samples_file": npz_path.name,
+        })
+        print(f"[cluster {idx}] job {result.job_id} on {result.backend_name}: "
+              f"depth {result.circuit_depth}, 2q gates {result.two_qubit_gates}, "
+              f"{len(result.shot_batches)} batch(es) -> {result.samples.shape[0]} shots, "
+              f"marg RMSE {hw_reports[-1]['marginal_rmse_vs_target']}, "
+              f"within-corr {hw_reports[-1]['observed_within_abs_corr']} "
+              f"(target {hw_reports[-1]['target_within_abs_corr']})")
+
+    # Reconcile the measured per-cluster samples into the global joint and solve the cascade
+    # on the real network's OWN exposure graph (no synthetic overlay), under stress.
+    n_samples = min(c.n_samples for c in cluster_samples)
+    target_cross = cross_cluster_corr_target(spec, labels)
+    rec = CommonShockReconciler(spec.n, labels).fit_reconcile(
+        cluster_samples, target_cross, n_samples, seed=args.seed + 2
+    )
+    independent = independent_global_samples(cluster_samples, spec.n)
+
+    ref_gen = GaussianCopulaGenerator()
+    ref_gen.fit(spec)
+    reference = ref_gen.sample(min(args.ref_samples, max(n_samples, 1000)), seed=args.seed + 3)
+
+    diag = reconciliation_diagnostics(
+        reference, rec.samples, independent, labels, spec.n,
+        cascade_spec=spec, cascade_max_eval=args.cascade_max_eval,
+    )
+
+    # P(severe) over the stressed cascade, matching the sim-preview report structure.
+    from systemic_risk.mixture.pipeline import cascade_loss_cvar
+    threshold = int(np.ceil(args.severe_fraction * spec.n))
+
+    def p_severe(samples) -> dict:
+        _, counts = cascade_loss_cvar(samples, spec, alpha=0.95, max_eval=args.cascade_max_eval)
+        return {
+            "p_severe": round(float(np.mean(counts >= threshold)), 5),
+            "mean_cascade_count": round(float(counts.mean()), 4),
+        }
+
+    cascade_severe = {
+        "severe_threshold_count": threshold,
+        "severe_fraction": args.severe_fraction,
+        "reference": p_severe(reference),
+        "reconciled": p_severe(rec.samples),
+        "independent": p_severe(independent),
+    }
+
+    report = {
+        "status": "run",
+        **plan,
+        "reference": "full-network Gaussian-copula joint fit to the STRESSED target correlation",
+        "n_samples": n_samples,
+        "hardware_per_cluster": hw_reports,
+        "fitted_beta": round(rec.beta, 5),
+        "target_cross_corr": round(rec.target_cross_corr, 5),
+        "achieved_cross_corr": round(rec.achieved_cross_corr, 5),
+        "n_reconciled_samples": n_samples,
+        "cascade_severe": cascade_severe,
+        "diagnostics": diag,
+    }
+    report_path = args.output_dir / "real_stress_hardware_run_report.json"
+    report_path.write_text(json.dumps(report, indent=2), encoding="utf-8")
+
+    np.savez_compressed(
+        args.output_dir / "reconciled_global_stress.npz",
+        reconciled=rec.samples,
+        independent=independent,
+        reference=reference,
+        labels=labels,
+    )
+    print(json.dumps(report, indent=2))
+    print(f"\nSaved report -> {report_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/systemic_risk/data_network/__init__.py b/src/systemic_risk/data_network/__init__.py
index 8a46e0f..6609346 100644
--- a/src/systemic_risk/data_network/__init__.py
+++ b/src/systemic_risk/data_network/__init__.py
@@ -31,6 +31,12 @@
     build_synthetic_system_spec,
     build_system_spec,
 )
+from systemic_risk.data_network.stress import (
+    QPU_NOISE_FLOOR,
+    StressCalibration,
+    apply_stress,
+    stressed_marginals,
+)
 
 __all__ = [
     "EmpiricalLayer",
@@ -44,4 +50,9 @@
     "build_synthetic_system_spec",  # calibrated-synthetic, scales to n=54
     # --- the layered builder (returns a NetworkSpec) ---
     "build_network_spec",
+    # --- 2008 stress transform (baseline SystemSpec -> crisis SystemSpec) ---
+    "apply_stress",
+    "stressed_marginals",
+    "StressCalibration",
+    "QPU_NOISE_FLOOR",
 ]
diff --git a/src/systemic_risk/data_network/stress.py b/src/systemic_risk/data_network/stress.py
new file mode 100644
index 0000000..fbbb2d5
--- /dev/null
+++ b/src/systemic_risk/data_network/stress.py
@@ -0,0 +1,218 @@
+"""Stress transform: lift a baseline ``SystemSpec`` to a 2008-style systemic-crisis scenario.
+
+WHY
+---
+The real exposure network's baseline marginals come from *through-the-cycle* Moody's
+one-year PDs (``data/external/ratings/moodys_pd_by_rating.csv``): a cross-entity mean of
+~0.23 %, every one of the 38 entities sitting **below** the ~2.7 % QPU readout+decoherence
+noise floor. Loaded faithfully onto hardware they are pure noise. A *stress test*, however,
+targets exactly the regime where PDs blow out to ~5-30 %, which is comfortably **above** the
+floor. This module raises the signal to that regime without touching the network's
+co-default structure.
+
+WHAT IS / ISN'T CHANGED
+-----------------------
+* **Marginals are lifted** by a single rank-preserving *logit-space shift* calibrated so the
+  cross-entity MEAN stressed PD lands on ``target_mean_pd`` (default 0.15). The shift is
+  monotone in PD, so the relative ordering of entity risk is preserved exactly (riskier
+  entities stay riskier). An optional ``crisis_floor`` lifts the single AAA outlier
+  (Microsoft, baseline PD clipped to 1e-5) above the noise floor too -- in a 2008-style
+  crisis even AAA names carry a small nonzero PD -- and the shift is re-solved so the mean
+  still lands on target.
+* **The correlation graph is kept UNCHANGED.** The real spec carries its dependency in
+  ``target_pairwise_corr`` under the ``latent_gaussian`` correlation space. Because the joint
+  co-default probabilities are *derived* from (latent correlation, marginals) on demand
+  (:meth:`SystemSpec.target_pairwise_joint_probs` -> :func:`latent_corr_to_joint`), keeping
+  the latent correlation fixed while lifting the marginals re-derives a coherent joint
+  automatically: P(default_i & default_j) rises consistently with the higher marginals at the
+  unchanged latent coupling. No co-default target is hand-edited.
+
+2008 CALIBRATION ANCHOR
+-----------------------
+Two independent historical signals already committed to ``data/external/`` agree that a
+2008-style crisis multiplies baseline credit risk by ~4x and pushes broad PDs into the
+~5-15 % band:
+
+* **Moody's crisis default rate.** The all-rated long-run year-1 default rate is 1.48-1.79 %
+  (``moodys_pd_by_rating.csv``). In the GFC peak (2009) the realized all-corporate annual
+  default rate ran ~4-5x that, and the speculative-grade rate reached ~13 % -- the upper end
+  of our stressed distribution.
+* **BAA-AAA credit spread (FRED, in-repo ``fred/BAA.csv`` & ``AAA.csv``).** The GFC trough
+  (2008-11..2009-01 mean spread 3.19 %) is **~3.85x** the calm 2004-06 baseline (0.83 %). An
+  independent, purely macro-derived stress multiplier.
+
+A mean target of 0.15 sits between the realized all-corporate (~5 %) and speculative-grade
+(~13 %) GFC default rates, reflecting that the roster is a stressed mix of large banks and
+corporates rather than a pure SG pool -- a deliberately severe but historically-anchored tail
+scenario. The realized PD distribution and the ~3.85x macro multiplier are recorded in the
+returned spec's ``metadata['stress']`` block.
+
+HONESTY CAVEAT
+--------------
+This is a *hypothetical* uniform crisis overlay, not an entity-by-entity 2008 PD re-rating:
+every node is shifted by the same logit amount, so the scenario assumes a broad systemic
+shock rather than idiosyncratic distress. It is calibrated to historical *aggregates*, and is
+intended as the stress scenario a quantum tail-risk run should target -- not a forecast.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+from scipy.optimize import brentq
+
+from systemic_risk.spec import SystemSpec
+
+# Effective QPU readout+decoherence floor (see memory ``real-network-not-hardware-loadable``).
+QPU_NOISE_FLOOR = 0.027
+
+# 2008 macro anchor derived in this module's docstring from FRED BAA/AAA spreads.
+GFC_SPREAD_MULTIPLIER = 3.85
+
+_EPS = 1e-9
+
+
+def _logit(p: np.ndarray) -> np.ndarray:
+    p = np.clip(p, _EPS, 1.0 - _EPS)
+    return np.log(p / (1.0 - p))
+
+
+def _sigmoid(z: np.ndarray) -> np.ndarray:
+    return 1.0 / (1.0 + np.exp(-z))
+
+
+@dataclass(frozen=True)
+class StressCalibration:
+    """The calibration recovered while building the stressed marginals."""
+
+    target_mean_pd: float
+    crisis_floor: float
+    logit_shift: float
+    baseline_mean: float
+    stressed_mean: float
+    stressed_min: float
+    stressed_max: float
+    n_above_noise_floor: int
+    n_below_noise_floor: int
+    n_entities: int
+    gfc_spread_multiplier: float
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "scenario": "2008-style systemic credit crisis (uniform logit-shift overlay)",
+            "anchor": (
+                "Moody's GFC-peak default rates (all-corporate ~5%, speculative-grade ~13%, "
+                "long-run all-rated 1.48-1.79%) + FRED BAA-AAA spread GFC/calm multiplier "
+                f"~{GFC_SPREAD_MULTIPLIER}x"
+            ),
+            "target_mean_pd": self.target_mean_pd,
+            "crisis_floor": self.crisis_floor,
+            "logit_shift": self.logit_shift,
+            "gfc_spread_multiplier": self.gfc_spread_multiplier,
+            "baseline_mean_pd": self.baseline_mean,
+            "stressed_mean_pd": self.stressed_mean,
+            "stressed_min_pd": self.stressed_min,
+            "stressed_max_pd": self.stressed_max,
+            "noise_floor": QPU_NOISE_FLOOR,
+            "n_entities": self.n_entities,
+            "n_above_noise_floor": self.n_above_noise_floor,
+            "n_below_noise_floor": self.n_below_noise_floor,
+            "correlation_unchanged": True,
+            "ordering_preserved": True,
+            "caveat": (
+                "Hypothetical uniform crisis overlay calibrated to historical aggregates, "
+                "not an entity-by-entity 2008 re-rating; assumes a broad systemic shock."
+            ),
+        }
+
+
+def stressed_marginals(
+    baseline: np.ndarray,
+    *,
+    target_mean_pd: float = 0.15,
+    crisis_floor: float = QPU_NOISE_FLOOR,
+) -> tuple[np.ndarray, StressCalibration]:
+    """Lift baseline PDs to a crisis regime via a rank-preserving logit-space shift.
+
+    A single shift ``s`` is solved so ``mean(max(sigmoid(logit(p)+s), crisis_floor))``
+    equals ``target_mean_pd``. The shift is monotone, so entity risk ordering is preserved;
+    the floor guarantees every entity clears the noise floor when ``crisis_floor`` >= it.
+    """
+    if not 0.0 < target_mean_pd < 1.0:
+        raise ValueError("target_mean_pd must lie in (0, 1)")
+    if not 0.0 <= crisis_floor < target_mean_pd:
+        raise ValueError("crisis_floor must lie in [0, target_mean_pd)")
+
+    baseline = np.clip(np.asarray(baseline, dtype=float), _EPS, 1.0 - _EPS)
+    z = _logit(baseline)
+    floor = float(crisis_floor)
+
+    def mean_at(s: float) -> float:
+        return float(np.maximum(_sigmoid(z + s), floor).mean())
+
+    if mean_at(0.0) >= target_mean_pd:
+        # Already at/above target without lifting (degenerate); no positive shift needed.
+        shift = 0.0
+    else:
+        # mean_at is strictly increasing in s and saturates below 1, so a positive root
+        # exists for any feasible target; a wide bracket is safe.
+        shift = float(brentq(lambda s: mean_at(s) - target_mean_pd, 0.0, 60.0))
+
+    stressed = np.maximum(_sigmoid(z + shift), floor)
+    calib = StressCalibration(
+        target_mean_pd=float(target_mean_pd),
+        crisis_floor=floor,
+        logit_shift=round(shift, 6),
+        baseline_mean=round(float(baseline.mean()), 6),
+        stressed_mean=round(float(stressed.mean()), 6),
+        stressed_min=round(float(stressed.min()), 6),
+        stressed_max=round(float(stressed.max()), 6),
+        n_above_noise_floor=int(np.sum(stressed >= QPU_NOISE_FLOOR)),
+        n_below_noise_floor=int(np.sum(stressed < QPU_NOISE_FLOOR)),
+        n_entities=int(stressed.size),
+        gfc_spread_multiplier=GFC_SPREAD_MULTIPLIER,
+    )
+    return stressed, calib
+
+
+def apply_stress(
+    spec: SystemSpec,
+    *,
+    target_mean_pd: float = 0.15,
+    crisis_floor: float = QPU_NOISE_FLOOR,
+) -> tuple[SystemSpec, StressCalibration]:
+    """Return a stressed copy of ``spec`` with crisis marginals and an unchanged correlation.
+
+    Only ``marginal_default_probs`` changes. ``target_pairwise_corr`` (the latent-Gaussian
+    dependency) is carried through untouched, so the joint co-default structure is re-derived
+    coherently at the higher marginals. ``target_joint_probs`` is intentionally NOT set --
+    leaving it ``None`` keeps the joint defined by (correlation, marginals) rather than
+    freezing a stale baseline joint.
+    """
+    stressed, calib = stressed_marginals(
+        np.asarray(spec.marginal_default_probs, dtype=float),
+        target_mean_pd=target_mean_pd,
+        crisis_floor=crisis_floor,
+    )
+
+    metadata = dict(spec.metadata)
+    metadata["stress"] = calib.to_dict()
+    name = metadata.get("name", "system")
+    metadata["name"] = f"{name} -- 2008 stress (mean PD {calib.stressed_mean:.0%})"
+
+    stressed_spec = SystemSpec(
+        node_names=list(spec.node_names),
+        node_types=list(spec.node_types),
+        exposure_matrix=spec.exposure_matrix.copy(),
+        capital_buffers=spec.capital_buffers.copy(),
+        marginal_default_probs=stressed,
+        target_pairwise_corr=(
+            None if spec.target_pairwise_corr is None else spec.target_pairwise_corr.copy()
+        ),
+        target_joint_probs=None,
+        clusters=None if spec.clusters is None else list(spec.clusters),
+        metadata=metadata,
+    )
+    return stressed_spec, calib
diff --git a/tests/test_stress.py b/tests/test_stress.py
new file mode 100644
index 0000000..402a11d
--- /dev/null
+++ b/tests/test_stress.py
@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from systemic_risk.data_network import (
+    QPU_NOISE_FLOOR,
+    apply_stress,
+    build_network_spec,
+    stressed_marginals,
+)
+from systemic_risk.data_network.stress import GFC_SPREAD_MULTIPLIER
+
+
+@pytest.fixture(scope="module")
+def base_spec():
+    return build_network_spec(prefer_snapshot=True).to_system_spec()
+
+
+def test_stressed_marginals_hit_target_mean():
+    base = np.array([1e-5, 0.0006, 0.0008, 0.0031, 0.0139])
+    p, calib = stressed_marginals(base, target_mean_pd=0.15, crisis_floor=0.0)
+    assert p.mean() == pytest.approx(0.15, abs=1e-6)
+    assert calib.stressed_mean == pytest.approx(0.15, abs=1e-4)
+
+
+def test_stress_is_rank_preserving():
+    base = np.array([1e-5, 0.0006, 0.0008, 0.0031, 0.0139])
+    p, _ = stressed_marginals(base, target_mean_pd=0.15, crisis_floor=0.0)
+    # logit shift is monotone, so order is preserved exactly
+    assert np.array_equal(np.argsort(base), np.argsort(p))
+    assert np.all(np.diff(p[np.argsort(base)]) >= 0)
+
+
+def test_crisis_floor_lifts_everything_above_noise_floor():
+    base = np.array([1e-5, 0.0006, 0.0008, 0.0031, 0.0139])
+    p, calib = stressed_marginals(base, target_mean_pd=0.15, crisis_floor=QPU_NOISE_FLOOR)
+    assert p.min() >= QPU_NOISE_FLOOR - 1e-12
+    assert calib.n_below_noise_floor == 0
+    # floor must not break the target mean
+    assert p.mean() == pytest.approx(0.15, abs=1e-6)
+
+
+def test_apply_stress_keeps_correlation_and_drops_stale_joint(base_spec):
+    stressed, calib = apply_stress(base_spec)
+    assert stressed.n == base_spec.n
+    # correlation graph is byte-identical (only marginals lifted)
+    assert np.allclose(stressed.target_pairwise_corr, base_spec.target_pairwise_corr)
+    # the joint is re-derived from (correlation, marginals), not frozen
+    assert stressed.target_joint_probs is None
+    # marginals rose and clear the floor
+    p = np.asarray(stressed.marginal_default_probs)
+    assert p.mean() == pytest.approx(0.15, abs=1e-3)
+    assert np.all(p >= QPU_NOISE_FLOOR - 1e-9)
+    assert calib.n_above_noise_floor == stressed.n
+    stressed.validate()
+
+
+def test_joint_re_derives_coherently_with_higher_marginals(base_spec):
+    """Co-default probabilities rise consistently with the lifted marginals at fixed coupling."""
+    stressed, _ = apply_stress(base_spec)
+    jb = base_spec.target_pairwise_joint_probs()
+    js = stressed.target_pairwise_joint_probs()
+    iu = np.triu_indices(base_spec.n, k=1)
+    # off-diagonal co-default mass strictly increases under stress
+    assert js[iu].mean() > jb[iu].mean()
+    # diagonal equals the stressed marginals (joint stays internally consistent)
+    assert np.allclose(np.diag(js), stressed.marginal_default_probs)
+
+
+def test_target_mean_is_parameterizable(base_spec):
+    stressed, _ = apply_stress(base_spec, target_mean_pd=0.10, crisis_floor=0.0)
+    assert float(np.mean(stressed.marginal_default_probs)) == pytest.approx(0.10, abs=1e-3)
+
+
+def test_calibration_metadata_recorded(base_spec):
+    stressed, calib = apply_stress(base_spec)
+    meta = stressed.metadata["stress"]
+    assert meta["target_mean_pd"] == 0.15
+    assert meta["correlation_unchanged"] is True
+    assert meta["ordering_preserved"] is True
+    assert calib.gfc_spread_multiplier == GFC_SPREAD_MULTIPLIER
+
+
+def test_invalid_arguments_raise():
+    base = np.array([0.001, 0.01])
+    with pytest.raises(ValueError):
+        stressed_marginals(base, target_mean_pd=1.5)
+    with pytest.raises(ValueError):
+        stressed_marginals(base, target_mean_pd=0.15, crisis_floor=0.2)