qkdpy 0.3.0

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 (now with individual gate classes for better modularity), multi-qubit states, and measurements (with flexible state collapse for research and visualization)
• QKD Protocols: Robust implementations of:
  • BB84 (Standard and Decoy-State variants)
  • E91 (Entanglement-based with true Bell state simulation)
  • B92 (Two-state protocol)
  • CV-QKD (Gaussian Modulated Coherent State with homodyne detection)
  • Device-Independent QKD (CHSH inequality verification)
  • HD-QKD (High-Dimensional QKD for prime dimensions)
  • Twisted Pair QKD (Experimental protocol)
• High-Dimensional QKD: Support for qudit-based protocols with enhanced security and key rates (currently optimized for prime dimensions)
• Key Management: Advanced error correction and privacy amplification algorithms
• Quantum Cryptography: Quantum authentication, key exchange, and random number generation
• Comprehensive Testing: Validated with a suite of over 200 tests covering security, integration, performance, and correctness.
• Enhanced Security: Cryptographically Secure Pseudo-Random Number Generation (CSPRNG), secure key rotation, and NIST-style randomness verification.
• Machine Learning Integration: Advanced parameter optimization using scikit-learn (Bayesian and Neural Networks).
• Quantum Networks: Physically accurate entanglement swapping and multi-party QKD simulation.

...

Enterprise Features

QKDpy now supports enterprise-grade simulation capabilities:

from qkdpy import QuantumNetwork, QKDOptimizer, QuantumKeyExchange, QuantumChannel

# 1. Optimize Network Parameters with ML
optimizer = QKDOptimizer("BB84")
best_params = optimizer.optimize_channel_parameters(
    {"loss": (0.0, 0.5)},
    objective_function,
    method="bayesian" # Uses Gaussian Process
)

# 2. Secure Key Exchange with Rotation
channel = QuantumChannel(loss=0.1)
exchange = QuantumKeyExchange(channel)
session_id = exchange.initiate_key_exchange("Alice", "Bob", 128)
exchange.execute_key_exchange(session_id)

# Rotate key for forward secrecy
exchange.rotate_key(session_id, new_key_length=256)

# 3. Entanglement Swapping in Networks
network = QuantumNetwork("EnterpriseNet")
network.add_node("Alice", position=(0,0))
network.add_node("Relay", position=(10,0))
network.add_node("Bob", position=(20,0))
network.add_connection("Alice", "Relay", 10)
network.add_connection("Relay", "Bob", 10)

# Teleport entanglement from Alice-Relay and Relay-Bob to Alice-Bob
success = network.perform_entanglement_swapping("Alice", "Bob")

• 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

# Create a virtual environment
uv venv

# Install in development mode
uv pip install -e .

Or using pip with a virtual environment:

# Create a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install in development mode
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, Qubit
from qkdpy.core import PauliX, Hadamard # Import individual gate classes

# 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']}")

# Example of flexible qubit measurement and collapse
q = Qubit.plus() # Qubit in superposition
print(f"Qubit state before measurement: {q.state}")
measurement_result = q.measure("hadamard") # Measure without collapsing internal state
print(f"Measurement result: {measurement_result}")
print(f"Qubit state after measurement (still in superposition): {q.state}")
q.collapse_state(measurement_result, "hadamard") # Explicitly collapse the state
print(f"Qubit state after explicit collapse: {q.state}")

# Example of applying a gate
q_zero = Qubit.zero()
print(f"Qubit state before X gate: {q_zero.state}")
q_zero.apply_gate(PauliX().matrix) # Apply Pauli-X gate
print(f"Qubit state after X gate: {q_zero.state}")

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.