quantumdlearning 1.0.1

A framework for quantum machine learning that integrates latest research results in quantum computing and deep learning.

QuantumDLearning

A framework for quantum machine learning that integrates the latest research results in quantum computing and deep learning.

Installation

From PyPI

pip install quantumdlearning

From Source

git clone https://github.com/quantumdlearning/quantumdlearning.git
cd quantumdlearning
pip install -e .

Quick Start

import quantumdlearning
import torch

# Create a quantum circuit
circuit = quantumdlearning.QubitCircuit(num_qubits=2)

# Add quantum gates
circuit.add_gate("H", [0])
circuit.add_gate("CNOT", [0, 1])

# Run the circuit
result = circuit.run()
print(result)

Features

• Quantum Circuits: Support for qubit circuits, distributed circuits, and photonic circuits
• Quantum Gates: Comprehensive library of quantum gates (Pauli, rotation, CNOT, etc.)
• Quantum Neural Networks: Advanced QNN models including quantum transformers
• Quantum Optimizers: Various quantum optimization algorithms
• Quantum Layers: Different types of quantum layers
• Mathematical Tools: Rich set of quantum mathematical functions
• Photonic Quantum Computing: Support for continuous-variable quantum computing
• Measurement-Based Quantum Computing: Support for MBQC
• Distributed Quantum Computing: Support for distributed quantum systems

Documentation

For detailed documentation, please visit QuantumDLearning Documentation.

Examples

See the examples/ directory for more examples.

Contributing

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

License

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

Citation

If you use QuantumDLearning in your research, please cite:

@software{quantumdlearning,
  title={QuantumDLearning: A Framework for Quantum Machine Learning},
  author={QuantumDLearning Team},
  year={2024},
  url={https://github.com/quantumdlearning/quantumdlearning}
}

Contact

For questions and support, please open an issue on GitHub or contact us at [e-mail].