TAGT-DSC-DMSG: Game-Theoretic Routing Simulator for Distributed Quantum Networks

This repository contains the official simulation framework for the Topology-Aware Game-Theoretic Routing with Dynamic State-Coupling and Decoherence-Mitigating Swapping Game (TAGT-DSC-DMSG) protocol.

The simulator evaluates entanglement distribution, memory buffer queue states, and recursive link state reliability across scale-free (Barabási-Albert), small-world (Watts-Strogatz), and random (Erdős-Rényi) quantum network topologies.

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Key Features

1. Stochastic Petri Net (SPN) Memory Model: Simulates node-level memory buffer occupancy, qubit generation/decoherence rates ($T_2$), and cryogenic cooling overheads.
2. Recursive Bayesian Link Estimation: Inters time-varying link success probabilities under non-Markovian noise using binary heralded swap attempt observations.
3. Decentralized Game-Theoretic routing: Formulates entanglement swapping as a non-cooperative game where repeaters act as rational players optimizing a multi-objective utility function (fidelity, energy, and latency).
4. Decoherence-Mitigating Swapping Game (DMSG): Implements queue-aware congestion avoidance that dynamically diverts swap paths away from overloaded hub repeaters to mitigate memory-time decoherence.

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Repository Structure

• quantum_simulator.py: Main Python simulator script containing network generators, SPN queuing logic, Bayesian filters, and the game-theoretic best-response solver.
• success_ratio.png: Simulation output plot comparing entanglement success rate across different network sizes.
• latency_comparison.png: Simulation output plot comparing average end-to-end latency under low, medium, and high congestion scenarios.

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Installation & Setup

Ensure you have Python 3.8+ installed. You can install the required dependencies using pip:

pip install numpy networkx matplotlib

Running the Simulation

To execute the Monte Carlo simulation and regenerate the performance plots, run:

python quantum_simulator.py

The script will:

1. Initialize the topologies (Scale-Free, Small-World, Random).
2. Execute $1000+$ Monte Carlo simulation steps under dynamic traffic workloads.
3. Solve for the Pure Strategy Nash Equilibrium (PSNE) at each routing interval.
4. Output the performance metrics and save the plots as success_ratio.png and latency_comparison.png.

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Simulation Results

1. Entanglement Success Rate

As shown in success_ratio.png, the TAGT-DSC-DMSG framework maintains an entanglement success rate above 92% for large scale-free networks ($N=300$). This represents a 20–22% improvement over standard greedy routing protocols, which suffer from buffer overflows and decoherence at central hub repeaters.

2. Latency under Congestion

As shown in latency_comparison.png, dynamic path redirection reduces end-to-end delay by 18% under high congestion, routing qubits around overloaded nodes to minimize memory waiting times.