Optimizing Variational Quantum Algorithms with qBANG: A Hybrid Approach to Tackle Flat Energy Landscapes
This repo contains the code for the optimizers introduced in the paper "Optimizing Variational Quantum Algorithms with qBANG: A Hybrid Approach to Tackle Flat Energy Landscapes" [[arXiv](UPDATE LINK)].
To use this code, you will need to set up a conda environment with Python 3.10. You can do this by running the following command: setup conda environment
conda create --name ENV_NAME python=3.10
conda activate ENV_NAME
The package qbang is prepared such that it can be installed in development mode locally (such that any change in the code is instantly reflected). To install qbang locally, clone/download the repository and run the following command from the qbang folder:
pip install -e .
Feel free to reach out to me at [email protected] if you face any issues.
To get started check out the examples/qBang_tutorial.ipynb notebook.
We welcome contributions to this project! If you'd like to contribute, please submit a pull request with your changes. If you have any questions or suggestions, please feel free to contact us at [email protected].
For testing and verification of the software run:
pytest src/qbang/tests
Variational quantum algorithms (VQAs) are a promising approach for solving complex optimization problems using quantum computers. However, these algorithms can struggle when faced with flat energy landscapes, where many parameter settings result in similar objective function values. qBang addresses this challenge by using a hybrid approach that combines classical optimization techniques with quantum information methods. By approximating the quantum Fisher information matrix, qBang is able to identify promising regions of the search space more efficiently than other optimizers.
The figure included in the readme file shows the workflow of the VQA with qBANG, highlighting the use of the optimizer.
We found several very helpful codebases when building this repo, and we sincerely thank their authors:
- PennyLane Tutorials:
If you find this repo useful for your research, please consider citing our paper:
@article{opt-vqa-with-qbang,
title={Optimizing Variational Quantum Algorithms with qBang: Efficiently Interweaving Metric and Momentum to Tackle Flat Energy Landscapes},
author={Fitzek, David and Jonsson, Robert S. and Dobrautz, Werner and Schäfer, Christian},
journal={arXiv preprint arXiv:2304.13882},
year={2023},
}Initial release (v1.0): April 2023
Current maintainers:
MIT License