A quantum-accelerated LLM prompt optimization system that uses QAOA (Quantum Approximate Optimization Algorithm) to solve the Maximum Independent Set problem across candidate prompts, selecting the highest-quality, non-redundant prompt for any given topic.
Built and tested on NVIDIA NGC using CUDA-Q cu12-0.9.0.
Six candidate prompts are scored on quality metrics, QAOA identifies and resolves redundancies, and the winning prompt is selected automatically.
Topic: Primal Sequence anime character profile
Candidates: 6
[Prompt Scorer] Scoring 6 candidate prompts...
Prompt 1: overall=39 | spec=14 clarity=75 rel=28
Prompt 2: overall=75 | spec=54 clarity=75 rel=100
Prompt 3: overall=24 | spec=10 clarity=60 rel=0
Prompt 4: overall=70 | spec=54 clarity=75 rel=84
Prompt 5: overall=42 | spec=22 clarity=75 rel=28
Prompt 6: overall=75 | spec=54 clarity=75 rel=100
[Quantum Prompt Optimizer] Conflicts detected: 1
- Prompt 1 vs Prompt 3: both low quality
[Quantum Prompt Optimizer] Optimal selection found at seed 0.
====================================================
WINNING PROMPT
====================================================
Write a detailed description of the character appearance,
personality, and motivations in The Primal Sequence anime series.
Quality Score: 75/100
Specificity : 54/100
Clarity : 75/100
Relevance : 100/100
Choosing the best prompt from many candidates is a combinatorial optimization problem. Given N candidate prompts, finding the most diverse, highest-quality subset requires evaluating exponentially many combinations — an NP-hard problem.
This system encodes prompt similarity and quality relationships as a conflict graph and uses QAOA to solve the Maximum Independent Set problem: finding the largest group of prompts where none are redundant with each other. The classical scorer then selects the single best prompt from the quantum-selected set.
This is a genuinely novel application of quantum computing — using quantum optimization not for physics or chemistry, but for natural language prompt engineering.
Input: Topic + N Candidate Prompts
|
Prompt Scorer (prompt_scorer.py)
- Specificity score (0-100)
- Clarity score (0-100)
- Relevance score (0-100)
|
Conflict Graph Builder
- Connects similar/redundant prompts
- Connects low-quality prompt pairs
|
QAOA Optimizer (quantum_optimizer.py)
- Maximum Independent Set via CUDA-Q
- Runs on NVIDIA NGC infrastructure
|
Classical Selector
- Picks highest scorer from quantum set
|
Output: Winning Prompt + Quality Breakdown + Log
The Maximum Independent Set problem is NP-hard. For N prompts, classical brute-force must check 2^N subsets. QAOA encodes the conflict graph as a Hamiltonian and uses quantum superposition to explore many subsets simultaneously, with quantum interference amplifying conflict-free solutions.
This is the same mathematical structure used in:
- Drug molecule optimization
- Financial portfolio selection
- Network design problems
Applied here to prompt engineering for the first time.
quantum-prompt-optimizer/
├── main.py -- full pipeline orchestrator
├── prompt_scorer.py -- quality scoring on 3 metrics
├── quantum_optimizer.py -- QAOA Maximum Independent Set solver
└── requirements.txt -- dependencies
| Tool | Role |
|---|---|
| NVIDIA CUDA-Q | Quantum circuit simulation |
| QAOA Algorithm | Maximum Independent Set optimization |
| Python 3.10 | Primary language |
| NVIDIA NGC | Cloud container infrastructure |
| Docker | Container runtime |
- Docker Desktop installed
- NVIDIA NGC account (free at ngc.nvidia.com)
docker pull nvcr.io/nvidia/quantum/cuda-quantum:cu12-0.9.0docker run --rm --entrypoint="" -v /your/local/path:/files \
nvcr.io/nvidia/quantum/cuda-quantum:cu12-0.9.0 \
python3 /files/main.pyEdit the candidates list and topic variable in main.py to optimize prompts for any domain.
Specificity (35% weight): Measures how detailed and precise the prompt is. Longer prompts with domain-specific keywords score higher.
Clarity (35% weight): Measures how unambiguous the prompt is. Vague words like "maybe", "something", "stuff" reduce the score. Direct action verbs and question marks increase it.
Relevance (30% weight): Measures how on-topic the prompt is for the given subject. Topic word matches and domain-specific terminology boost the score.
Maximum Independent Set: Given a graph where nodes are prompts and edges connect redundant or low-quality pairs, the Maximum Independent Set is the largest group of nodes with no edges between them — the most diverse, non-redundant subset of prompts.
Hamiltonian Encoding: Each conflict edge (prompt_a, prompt_b) becomes a term 0.5 * Z(a) * Z(b) in the cost Hamiltonian. Minimizing energy selects the maximum independent set.
Conflict Graph Construction: Two prompts are connected if their Jaccard similarity exceeds 0.3 (too redundant) or if both score below 40/100 (both too weak to include together).
- AI Content Creation: Select the best prompt for character descriptions, world-building, dialogue generation
- Research: Automatically select the most informative query from a set of candidates
- Education: Optimize exam questions for clarity and specificity
- Enterprise: Select the most precise prompt for customer service or document generation pipelines
| Project | Algorithm | Problem Type | New Concept |
|---|---|---|---|
| Quantum Scheduler | QAOA | Graph Coloring | Baseline QAOA |
| Multi-Agent Scheduler | QAOA + Classical | Graph Coloring + 3-color | Multi-agent coordination |
| Quantum Prompt Optimizer | QAOA | Maximum Independent Set | Novel NLP application |
Joshua Ritz — github.com/21jritz
Built on NVIDIA NGC | CUDA-Q cu12-0.9.0 | Novel Quantum-NLP Application