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A comprehensive, production-grade Python monorepo for quantum computing research at the intersection of quantum mechanics and artificial intelligence. Eight self-contained modules covering the full spectrum of quantum AI — from cryptography to computer vision, from chaos theory to reinforcement learning.
No quantum hardware required. Every algorithm runs on classical hardware via statevector simulation.
Getting Started • Modules • Architecture • Examples • Contributing
- 8 Research Modules — covering the complete landscape of Quantum AI
- Pure NumPy/SciPy — no quantum hardware or SDK dependencies needed
- Mathematically Rigorous — every algorithm derived from first principles
- Educational — comprehensive docstrings with LaTeX-style formulas
- Runnable Examples — every module ships with executable demos
- Production Architecture — clean interfaces, type hints, modular design
| Module | Description | Key Algorithms |
|---|---|---|
| Quantum ML | Quantum Machine Learning | Quantum Kernels, VQC, QNN |
| Quantum Chaos | Quantum Chaos Theory | Kicked Top, Level Spacing, OTOC |
| Quantum Crypto | Quantum Cryptography | BB84, E91, Quantum OTP |
| Quantum Algorithms | Fundamental Algorithms | Grover, Shor, QFT, Deutsch-Jozsa |
| Quantum Simulation | Many-Body Simulation | Ising, Hubbard, Trotter |
| Quantum Optimization | Combinatorial Optimization | QAOA, VQE, MaxCut |
| Quantum RL | Reinforcement Learning | Quantum Policy, Hybrid Agent |
| Quantum Vision | Quantum Computer Vision | Quanvolution, FRQI, Hybrid CNN |
Python >= 3.10# Clone the repository
git clone https://github.com/your-org/quantum-ai-lab.git
cd quantum-ai-lab
# Install dependencies
pip install -r requirements.txt
# Install as package (development mode)
pip install -e .# Generate a quantum-secure key with BB84
from quantum_crypto import BB84Protocol
bb84 = BB84Protocol()
key, stats = bb84.run_protocol(n_qubits=1000)
print(f"Shared key length: {stats.final_key_length} bits")
print(f"QBER: {stats.qber:.4f}")
print(f"Protocol secure: {stats.protocol_secure}")
# Factor a number with Shor's Algorithm
from quantum_algorithms import ShorFactoring
shor = ShorFactoring()
result = shor.factor(15)
p, q = result.factors
print(f"15 = {p} x {q} (success={result.success})")
# Train a Variational Quantum Classifier
from quantum_ml import VariationalClassifier
import numpy as np
X_train = np.random.rand(20, 2) * np.pi
y_train = (X_train[:, 0] > np.pi / 2).astype(int)
clf = VariationalClassifier(n_qubits=2, n_layers=2, random_state=42)
clf.train(X_train, y_train, epochs=50, verbose=False)
print(f"Train accuracy: {clf.accuracy(X_train, y_train):.1%}")
# Simulate an Ising Model Phase Transition
from quantum_simulation import IsingModel
ising = IsingModel(n_sites=6, J=1.0, h=0.5)
E0, psi0 = IsingModel.ground_state(ising.hamiltonian)
mag_profile = IsingModel.magnetization(psi0, ising.n_sites)
print(f"Ground state energy: {E0:.4f}")
print(f"Avg magnetization: {mag_profile.mean():.4f}")graph TB
subgraph "Quantum AI Laboratory"
direction TB
subgraph "Core Layer"
ML["Quantum ML"]
ALG["Algorithms"]
SIM["Simulation"]
end
subgraph "Application Layer"
CRYPTO["Cryptography"]
OPT["Optimization"]
RL["Reinforcement Learning"]
VISION["Vision"]
end
subgraph "Theory Layer"
CHAOS["Chaos"]
end
ALG --> ML
ALG --> CRYPTO
ALG --> OPT
SIM --> CHAOS
SIM --> OPT
ML --> RL
ML --> VISION
end
subgraph "Foundation"
NP["NumPy"]
SP["SciPy"]
MPL["Matplotlib"]
end
ML --> NP
ALG --> NP
SIM --> SP
CRYPTO --> NP
OPT --> SP
RL --> NP
VISION --> NP
CHAOS --> SP
SIM --> MPL
quantum-ai-lab/
├── README.md ← You are here
├── LICENSE
├── pyproject.toml
├── requirements.txt
├── docs/
│ └── architecture.md
│
├── quantum_ml/ # Quantum Machine Learning
│ ├── qkernel.py # Quantum Kernel Methods
│ ├── variational_classifier.py # Variational Quantum Classifier
│ ├── quantum_neural_net.py # Quantum Neural Network
│ └── examples/
│
├── quantum_chaos/ # Quantum Chaos Theory
│ ├── kicked_top.py # Kicked Top Model
│ ├── level_spacing.py # Level Spacing Statistics
│ ├── lyapunov.py # Quantum Lyapunov Exponents
│ └── examples/
│
├── quantum_crypto/ # Quantum Cryptography
│ ├── bb84.py # BB84 QKD Protocol
│ ├── e91.py # E91 Protocol
│ ├── quantum_otp.py # Quantum One-Time Pad
│ └── examples/
│
├── quantum_algorithms/ # Fundamental Quantum Algorithms
│ ├── grover.py # Grover's Search
│ ├── shor.py # Shor's Factoring
│ ├── qft.py # Quantum Fourier Transform
│ ├── deutsch_jozsa.py # Deutsch-Jozsa Algorithm
│ └── examples/
│
├── quantum_simulation/ # Quantum Many-Body Simulation
│ ├── ising_model.py # Transverse-Field Ising Model
│ ├── hubbard_model.py # Fermi-Hubbard Model
│ ├── trotter.py # Trotter Decomposition
│ └── examples/
│
├── quantum_optimization/ # Quantum Optimization
│ ├── qaoa.py # QAOA
│ ├── vqe.py # VQE
│ ├── max_cut.py # MaxCut Solver
│ └── examples/
│
├── quantum_rl/ # Quantum Reinforcement Learning
│ ├── quantum_policy.py # Quantum Policy Network
│ ├── hybrid_agent.py # Hybrid RL Agent
│ ├── quantum_environment.py # Environment Wrapper
│ └── examples/
│
└── quantum_vision/ # Quantum Computer Vision
├── quanvolution.py # Quanvolutional Networks
├── quantum_encoder.py # Image Encoding
├── hybrid_classifier.py # Hybrid Classifier
└── examples/
Each module includes runnable examples in its examples/ directory:
# Quantum Machine Learning — Iris Classification
python -m quantum_ml.examples.iris_classification
# Quantum Chaos — Visualization
python -m quantum_chaos.examples.chaos_visualization
# Quantum Cryptography — Secure Channel
python -m quantum_crypto.examples.secure_channel_demo
# Quantum Algorithms — Factoring
python -m quantum_algorithms.examples.factoring_demo
# Quantum Simulation — Phase Transitions
python -m quantum_simulation.examples.phase_transition_demo
# Quantum Optimization — Portfolio Optimization
python -m quantum_optimization.examples.portfolio_optimization
# Quantum RL — CartPole with Quantum Agent
python -m quantum_rl.examples.cartpole_quantum
# Quantum Vision — MNIST with Quantum Filters
python -m quantum_vision.examples.mnist_quantumThis repository implements quantum algorithms from first principles. Key mathematical concepts:
| Concept | Notation | Module |
|---|---|---|
| State Vector | |ψ⟩ = Σ αᵢ|i⟩ | All modules |
| Unitary Evolution | |ψ(t)⟩ = U(t)|ψ(0)⟩ | Simulation, Algorithms |
| Quantum Kernel | K(x,y) = |⟨φ(x)|φ(y)⟩|² | ML |
| CHSH Inequality | S = |E(a,b) - E(a,b') + E(a',b) + E(a',b')| ≤ 2 | Crypto |
| Grover Iterate | G = (2|ψ⟩⟨ψ| - I) · Oₓ | Algorithms |
| Trotter Formula | e^{i(A+B)t} ≈ (e^{iAt/n}e^{iBt/n})ⁿ | Simulation |
| OTOC | C(t) = -⟨[W(t), V(0)]²⟩ | Chaos |
| QAOA | |γ,β⟩ = e^{-iβₚB}e^{-iγₚC}···e^{-iβ₁B}e^{-iγ₁C}|+⟩ | Optimization |
| Component | Technology | Purpose |
|---|---|---|
| Core Language | Python 3.10+ | All implementations |
| Linear Algebra | NumPy | State vectors, gates, operators |
| Scientific Computing | SciPy | Optimization, eigensolvers, matrix exponentials |
| Visualization | Matplotlib | Plots, diagrams, quantum state visualization |
| Quantum (Optional) | PennyLane / Qiskit | Real quantum hardware backends |
This project is licensed under the MIT License — see the LICENSE file for details.
- Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information. Cambridge University Press.
- Schuld, M., & Petruccione, F. (2021). Machine Learning with Quantum Computers. Springer.
- Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum, 2, 79.
- Farhi, E., Goldstone, J., & Gutmann, S. (2014). A Quantum Approximate Optimization Algorithm. arXiv:1411.4028.
- Havlíček, V., et al. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567, 209-212.
- Haake, F. (2010). Quantum Signatures of Chaos. Springer.
- Bennett, C. H., & Brassard, G. (1984). Quantum cryptography: Public key distribution and coin tossing. TCS, 560, 7-11.
- Henderson, M., et al. (2020). Quanvolutional neural networks. Quantum Science and Technology, 5(3).
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
This repository is currently under active development. Features and APIs are subject to change as we continue to expand and refine the implementations.
Quantum AI Laboratory — Exploring the frontiers of quantum intelligence