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}
}
1 maintainer
Metadata
1 maintainer
Release files for qandle 0.1.12
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|---|---|---|---|---|
| qandle-0.1.12-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 68.5 kB
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