QANDLE

QANDLE is a fast and simple quantum state-vector simulator for hybrid machine learning using the PyTorch library. Documentation and examples can be found in the QANDLE documentation, the code resides on GitHub. The paper can be found on arXiv.

Installation

pip install qandle

Usage

import torch
import qandle

# Create a quantum circuit
circuit = qandle.Circuit(
    layers=[
        # embedding layer
        qandle.AngleEmbedding(num_qubits=2),
        # trainable layer, with random initialization
        qandle.RX(qubit=1),
        # trainable layer, with fixed initialization
        qandle.RY(qubit=0, theta=torch.tensor(0.2)),
        # data reuploading
        qandle.RX(qubit=0, name="data_reuploading"),
        # disable quantum weight remapping
        qandle.RY(qubit=1, remapping=None),
        qandle.CNOT(control=0, target=1),
        qandle.MeasureProbability(),
    ]
)


input_state = torch.rand(circuit.num_qubits, dtype=torch.float) # random input
data_reuploading = torch.rand(1, dtype=torch.float) # random data reuploading input

# Run the circuit
circuit(input_state, data_reuploading=data_reuploading)

License

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

Citation

If you use QANDLE in your research, please cite the following paper:

@misc{qandle2024,
      title={Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting}, 
      author={Gerhard Stenzel and Sebastian Zielinski and Michael Kölle and Philipp Altmann and Jonas Nüßlein and Thomas Gabor},
      year={2024},
      eprint={2404.09213},
      archivePrefix={arXiv},
      primaryClass={quant-ph}
}