quant-gameth 0.1.0

A novel Quantum-Game Theory framework for complex problem solving

⚛️ quant-gameth

Quantum-Game Theory Framework
Solve combinatorial optimization & game theory problems with quantum-inspired algorithms

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                         quant-gameth Framework                          │
├──────────────────────┬────────────────────────┬─────────────────────────┤
│   ⚛️ QUANTUM ENGINE   │  🎯 GAME THEORY ENGINE │   🔗 PROBLEM ENCODERS   │
│                      │                        │                         │
│  state.py            │  normal_form.py        │  qubo.py                │
│  gates.py            │  extensive_form.py     │  constraints.py         │
│  circuit.py          │  minimax.py            │  game_bridge.py         │
│  measurement.py      │  evolutionary.py       │  strategy_mapper.py     │
│  grover.py           │  mechanism.py          │                         │
│  qaoa.py             │  quantum_games.py      │                         │
│  vqe.py              │  repeated.py           │                         │
│  annealing.py        │  cooperative.py        │                         │
│  ansatz.py           │                        │                         │
│  tensor_network.py   │                        │                         │
├──────────────────────┴────────────────────────┴─────────────────────────┤
│                         🧩 APPLICATION SOLVERS                          │
│  sudoku · graph_coloring · maxcut · nqueens · knapsack · tsp            │
│  portfolio                                                              │
├──────────────────────┬────────────────────────┬─────────────────────────┤
│   📊 DATA GENERATORS  │   📈 VISUALIZATION     │   ⏱️ METRICS & BENCH    │
│  puzzles.py          │  quantum_viz.py        │  performance.py         │
│  graphs.py           │  game_viz.py           │  benchmark.py           │
│  games_gen.py        │  solver_viz.py         │                         │
│  market.py           │                        │                         │
├──────────────────────┴────────────────────────┴─────────────────────────┤
│                         ⚙️ INFRASTRUCTURE                               │
│  backends/ (classical · gpu · hybrid)                                   │
│  utils/   (serialization · decomposition)                               │
│  _types.py (core dataclasses & enums)                                   │
└─────────────────────────────────────────────────────────────────────────┘

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✨ Key Features

⚛️ Quantum Algorithms Statevector simulation — Clifford+T + parameterised gates, up to ~22 qubits QAOA — multi-layer, warm-start, custom mixers VQE — variational eigensolver with parameter shift gradients Grover's search — amplitude amplification, multi-target Quantum annealing — simulated QA + parallel tempering Tensor networks — MPS backend for n>20 qubits Ansatz library — HEA, QAOA, QAOA+, UCC

🎯 Game Theory Nash equilibria — support enumeration, Lemke-Howson, LP Extensive-form — backward induction, subgame perfect equilibrium Evolutionary — replicator dynamics, Moran process, ESS Mechanism design — 1st-price, Vickrey, VCG, English auctions Quantum games — EWL protocol, quantum PD, penny flip Repeated games — iterated PD, 10+ strategies, tournaments Cooperative — Shapley value, core, nucleolus

🧩 Application Solvers MaxCut — QAOA, simulated annealing, brute-force Graph Coloring — backtracking, QAOA, SA TSP — nearest-neighbour, 2-opt, SA, QAOA Knapsack — DP, branch-and-bound, QUBO annealing Sudoku — constraint propagation, backtracking N-Queens — backtracking, simulated annealing Portfolio — Markowitz, discrete selection, SA

⚙️ Infrastructure 3 backends — Classical (NumPy), GPU (CuPy), Hybrid (auto-dispatch) Decomposition — divide-and-conquer, sliding window, hierarchical Benchmarking — automated sweeps, JSON/CSV export Serialization — save/load results, circuits, states Performance — timing, memory profiling, approximation ratios

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🚀 Installation

# Clone the repository
git clone https://github.com/Ronit26Mehta/quant-gameth.git
cd quant-gameth

# Install in editable mode
pip install -e .

# (Optional) GPU acceleration
pip install cupy-cuda12x

Requirements

Package	Minimum Version
Python	≥ 3.9
NumPy	≥ 1.21
SciPy	≥ 1.7
Matplotlib	≥ 3.5
NetworkX	≥ 2.6

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📖 Quick Start

1. Quantum Circuit — Bell State

from quant_gameth.quantum.circuit import QuantumCircuit, Simulator
from quant_gameth.quantum.measurement import sample_counts

qc = QuantumCircuit(2)
qc.h(0).cx(0, 1)           # Hadamard + CNOT → Bell state |Φ+⟩

sv = Simulator().run(qc)
counts = sample_counts(sv, n_shots=1024)
print(counts)               # {'00': ~512, '11': ~512}

2. Nash Equilibrium — Prisoner's Dilemma

from quant_gameth.games.normal_form import NormalFormGame

game = NormalFormGame.prisoners_dilemma()
equilibria = game.find_nash()

for eq in equilibria:
    print(f"Strategies: {eq.strategies}")
    print(f"Payoffs:    {eq.payoffs}")

3. MaxCut with QAOA

import numpy as np
from quant_gameth.solvers.maxcut import solve_maxcut

adj = np.array([[0,1,1],[1,0,1],[1,1,0]], dtype=float)
result = solve_maxcut(adj, method="qaoa", qaoa_depth=3)
print(f"Cut value: {result.metadata['cut_value']}")
print(f"Partition: {result.solution}")

4. Portfolio Optimization

from quant_gameth.generators.market import generate_portfolio_data
from quant_gameth.solvers.portfolio import solve_portfolio

mu, sigma = generate_portfolio_data(n_assets=10, seed=42)
result = solve_portfolio(mu, sigma, risk_aversion=1.0, method="markowitz")
print(f"Optimal weights: {result.solution.round(4)}")
print(f"Sharpe ratio:    {result.metadata['sharpe_ratio']:.4f}")

5. Sudoku Solver

from quant_gameth.generators.puzzles import generate_sudoku
from quant_gameth.solvers.sudoku import solve_sudoku

board = generate_sudoku(difficulty="hard", seed=42)
result = solve_sudoku(board, method="backtracking")
print(result.solution.reshape(9, 9))

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🎮 Demo Scripts

Run any demo directly:

python -m quant_gameth.examples.demo_quantum
python -m quant_gameth.examples.demo_games
python -m quant_gameth.examples.demo_sudoku
python -m quant_gameth.examples.demo_maxcut
python -m quant_gameth.examples.demo_portfolio
python -m quant_gameth.examples.demo_trading
python -m quant_gameth.examples.demo_tournament

Script	What It Demonstrates
demo_quantum	States, Bell state, Grover's search, QAOA, VQE
demo_games	Nash equilibria, backward induction, evolutionary dynamics, Shapley values
demo_sudoku	Puzzle generation (easy/medium/hard) + solving
demo_maxcut	Brute-force vs QAOA vs SA with approximation ratios
demo_portfolio	Markowitz, discrete selection, efficient frontier
demo_trading	First-price, Vickrey, VCG, English auctions
demo_tournament	Iterated PD round-robin + evolutionary dynamics

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📊 Benchmarking

from quant_gameth.metrics.benchmark import BenchmarkSuite, BenchmarkConfig

suite = BenchmarkSuite()
suite.register_builtin("maxcut")

results = suite.run(BenchmarkConfig(
    problem_name="maxcut",
    sizes=[6, 8, 10, 12],
    methods=["qaoa", "annealing", "brute_force"],
    n_repeats=5,
))

suite.export_json(results, "benchmark_results.json")
suite.export_csv(results,  "benchmark_results.csv")

Expected Performance

Problem	Size	Classical Baseline	Framework	Speedup
MaxCut (dense)	100 nodes	~10s	~5s	2×
Sudoku (hard)	729 variables	~60s	~30s	2×
Portfolio	50 assets	~1s	~0.5s	2×
2-player game	10×10	~0.1s	~0.05s	2×

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🔬 Novel Contributions

#	Innovation	Description
1	Quantum-Game Bridge	Auto-transpile any normal-form game → QUBO or quantum circuit
2	EWL Quantum Games	Full EWL protocol with demonstrated quantum advantage over classical Nash
3	Hybrid Quantum-Evolutionary	Combine replicator dynamics with quantum annealing for equilibrium discovery
4	Game-Theoretic QAOA	QAOA warm-start from game best-response strategies
5	Multi-Agent Tournament	Tournament system with evolving quantum strategies across generations

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📁 Project Structure

quant-gameth/
├── pyproject.toml                    # Build config & dependencies
├── LICENSE                           # MIT License
├── README.md                         # This file
├── quant_gameth/
│   ├── __init__.py
│   ├── _types.py                     # Core dataclasses & enums
│   ├── quantum/                      # Quantum simulation engine (11 modules)
│   ├── games/                        # Game theory engine (8 modules)
│   ├── encoders/                     # Problem encoders (4 modules)
│   ├── solvers/                      # Application solvers (7 modules)
│   ├── generators/                   # Data generators (4 modules)
│   ├── viz/                          # Visualization (3 modules)
│   ├── metrics/                      # Performance & benchmarking (2 modules)
│   ├── backends/                     # Execution backends (4 modules)
│   ├── utils/                        # Utilities (2 modules)
│   └── examples/                     # Runnable demos (7 scripts)

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

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Made with ❤️ for quantum computing and game theory research
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