qkdpy 0.2.1

A Python Package for Quantum Key Distribution

QKDpy: Quantum Key Distribution Library

QKDpy is a comprehensive Python library for Quantum Key Distribution (QKD) simulations, implementing various QKD protocols, quantum simulators, and cryptographic tools. It provides an intuitive API similar to NumPy, TensorFlow, and scikit-learn, making quantum cryptography accessible to developers and researchers.

Features

• Quantum Simulation: Simulate qubits, quantum gates, multi-qubit states, and measurements
• QKD Protocols: Implementations of BB84, E92, E91, SARG04, CV-QKD, Device-Independent QKD, HD-QKD, and more
• High-Dimensional QKD: Support for qudit-based protocols with enhanced security and key rates
• Key Management: Advanced error correction and privacy amplification algorithms
• Quantum Cryptography: Quantum authentication, key exchange, and random number generation
• Enhanced Security: Message authentication, key validation, and side-channel protection
• Machine Learning Integration: Optimization and anomaly detection for QKD systems
• Quantum Networks: Multi-party QKD and network simulation capabilities
• Visualization: Advanced tools to visualize quantum states and protocol execution
• Quantum Network Analysis: Tools for analyzing quantum networks and multi-party QKD
• Extensible Design: Easy to add new protocols and features
• Performance: Efficient implementations for simulations

Installation

QKDpy requires Python 3.10 or higher. We recommend using uv for package management:

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository
git clone https://github.com/yourusername/qkdpy.git
cd qkdpy

# Install in development mode
uv pip install -e .

Installation

QKDpy requires Python 3.10 or higher. We recommend using uv for package management:

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository
git clone https://github.com/yourusername/qkdpy.git
cd qkdpy

# Install in development mode
uv pip install -e .

Quick Start

Here's a simple example of using the BB84 protocol to generate a secure key:

from qkdpy import BB84, QuantumChannel

# Create a quantum channel with some noise
channel = QuantumChannel(loss=0.1, noise_model='depolarizing', noise_level=0.05)

# Create a BB84 protocol instance
bb84 = BB84(channel, key_length=100)

# Execute the protocol
results = bb84.execute()

# Print the results
print(f"Generated key: {results['final_key']}")
print(f"QBER: {results['qber']:.4f}")
print(f"Is secure: {results['is_secure']}")

For High-Dimensional QKD:

from qkdpy import HDQKD, QuantumChannel

# Create a quantum channel with some noise
channel = QuantumChannel(loss=0.1, noise_model='depolarizing', noise_level=0.05)

# Create an HD-QKD protocol instance with 4-dimensional qudits
hd_qkd = HDQKD(channel, key_length=100, dimension=4)

# Execute the protocol
results = hd_qkd.execute()

# Print the results
print(f"Generated key: {results['final_key']}")
print(f"QBER: {results['qber']:.4f}")
print(f"Is secure: {results['is_secure']}")
print(f"Dimensional efficiency gain: {hd_qkd.get_dimension_efficiency():.2f}x")

For more examples, see the examples directory.

Advanced Usage

QKDpy also supports advanced protocols and features:

from qkdpy import (
    DeviceIndependentQKD,
    QuantumKeyManager,
    QuantumRandomNumberGenerator,
    QuantumNetwork,
    HDQKD,
    QKDOptimizer
)

# Device-independent QKD
di_qkd = DeviceIndependentQKD(channel, key_length=100)
results = di_qkd.execute()

# Quantum key management
key_manager = QuantumKeyManager(channel)
key_id = key_manager.generate_key("secure_session", key_length=128)

# Quantum random number generation
qrng = QuantumRandomNumberGenerator(channel)
random_bits = qrng.generate_random_bits(100)

# Quantum network simulation
network = QuantumNetwork("Research Network")
network.add_node("Alice")
network.add_node("Bob")
network.add_connection("Alice", "Bob", channel)
key = network.establish_key_between_nodes("Alice", "Bob", 128)

# High-dimensional QKD
hd_qkd = HDQKD(channel, key_length=100, dimension=4)
hd_results = hd_qkd.execute()

# ML-based QKD optimization
optimizer = QKDOptimizer("BB84")
parameter_space = {
    "loss": (0.0, 0.5),
    "noise_level": (0.0, 0.1)
}
# optimization_results = optimizer.optimize_channel_parameters(
#     parameter_space,
#     lambda params: simulate_protocol_performance(params),
#     num_iterations=50
# )

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

QKDpy is licensed under the Apache License 2.0. See LICENSE for the full license text.

Citation

If you use QKDpy in your research, please cite it as described in CITATION.cff.

Code of Conduct

This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code.

Contact

For questions, suggestions, or issues, please open an issue on GitHub.