This repository contains the code accompanying the article on hybrid quantum-classical simulation of quantum many-body dynamics. The study presents a novel approach to overcoming current limitations in quantum computing by leveraging both quantum and classical computational resources. Our method utilizes Trotterization to evolve an initial state on a quantum computer, focusing on the Hamiltonian terms that are difficult to simulate classically. A classical model then corrects the quantum simulation by incorporating the omitted terms. This hybrid approach enhances scalability and mitigates noise while avoiding variational parameter optimization in the quantum circuit.
cqd/
- Contains the core implementationexpectation/
- Pauli strings and sums in the CQD framework, computation of expectation values by sampling from the quantum circuitforces/
- Contains the code to compute the forces and the quantum geometric tensor using the CQD ansatztdvp/
- TDVP classes for one and two subsystems to run the simulationmodels/
- CQD Ansatz for one and two subsystems as well as a range of classical ansatze to plug into the frameworkintegrators/
- Additional implicit Runge-Kutta integrators that are compatible with this frameworkutils.py
- Small helper functions used throughout the codebase
examples/
- Example codes used to simulations in the paper
To run the code, ensure you have Python installed along with the required dependencies. To install the package, clone this repository and run:
pip install -e /path/to/cqd
If you use this code in your research, please cite our work:
@misc{gentinetta2025cqd,
title={Correcting and extending Trotterized quantum many-body dynamics},
author={Gian Gentinetta and Friederike Metz and Giuseppe Carleo},
year={2025},
eprint={2502.13784},
archivePrefix={arXiv},
primaryClass={quant-ph},
url={https://arxiv.org/abs/2502.13784},
}
This repository is licensed under the Apache License, Version 2.0. See LICENSE
for details.
For questions or contributions, please contact the authors or open an issue in the repository.