https://github.com/stfc/quantum-molecular-encodings
Data and codes used in Boy et al. (2025) - Quantum molecular structure encoding
https://github.com/stfc/quantum-molecular-encodings
encoding-algorithms molecular-representation molecular-structures quantum-computing quantum-machine-learning
Last synced: 5 months ago
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Data and codes used in Boy et al. (2025) - Quantum molecular structure encoding
- Host: GitHub
- URL: https://github.com/stfc/quantum-molecular-encodings
- Owner: stfc
- License: apache-2.0
- Created: 2025-07-09T13:18:16.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-12-10T22:12:22.000Z (6 months ago)
- Last Synced: 2025-12-11T09:48:36.350Z (6 months ago)
- Topics: encoding-algorithms, molecular-representation, molecular-structures, quantum-computing, quantum-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 5.72 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.bib
Awesome Lists containing this project
README
Quantum molecular encodings
Code and data from the publication by Boy et al. (2025)
Key features •
How to use •
How to cite •
Acknowledgements •
Related •
License
## Key Features
> The link to the corresponding paper can be found on: https://arxiv.org/abs/2507.20422
>
> The full data can be accessed here on Zenodo: https://zenodo.org/records/16520498
## How to cite
```bibtex
@ARTICLE{2025arXiv250720422B,
author = {{Boy}, Choy and {Altamura}, Edoardo and {Manawadu}, Dilhan and {Tavernelli}, Ivano and {Mensa}, Stefano and {Wales}, David J.},
title = "{Encoding molecular structures in quantum machine learning}",
journal = {arXiv e-prints},
keywords = {Quantum Physics},
year = 2025,
month = jul,
eid = {arXiv:2507.20422},
pages = {arXiv:2507.20422},
archivePrefix = {arXiv},
eprint = {2507.20422},
primaryClass = {quant-ph},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv250720422B},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```
## How to use
To clone and run this application, you'll need [Git](https://git-scm.com) and [Python 3](https://www.python.org/downloads/) installed on your computer. From your command line:
```bash
# Clone this repository
$ git clone https://github.com/stfc/quantum-molecular-encodings
# Go into the repository
$ cd quantum-molecular-encodings
# (Optional) Create a virtual environment
$ python -m venv venv-me
# Install dependencies
$ pip install -r requirements.txt
# Run the app
$ python example.py
```
## Acknowledgements
We are grateful to M. Emre Sahin for helping draft the code to input quantum circuits as feature maps in Qiskit Machine Learning. We acknowledge useful conversations with Jason Crain, and help from Edward O. Pyzer-Knapp and Benjamin C. B. Symons during the initial set-up of the project.
This work was supported by the Hartree National Centre for Digital Innovation, a UK Government-funded collaboration between STFC and IBM.
IBM, the IBM logo, and [www.ibm.com](https://ibm.com) are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. The current list of IBM trademarks is available at [www.ibm.com/legal/copytrade](https://www.ibm.com/legal/copytrade).
## Related
Check out [Qiskit Machine Learning](https://github.com/qiskit-community/qiskit-machine-learning), the library that powered this project.
If you use Qiskit Machine Learning in your work, please consider citing the relevant "overview" paper: [Sahin et al. 2025](https://www.arxiv.org/abs/2505.17756). [](https://arxiv.org/abs/2505.17756)
## License
This project uses the [Apache License 2.0](LICENSE).
> [hartree.stfc.ac.uk](https://www.hartree.stfc.ac.uk/) ·
> [ch.cam.ac.uk](https://www.ch.cam.ac.uk/) ·
> GitHub [@stfc](https://github.com/stfc) [@edoaltamura](https://github.com/edoaltamura) [@bc537](https://github.com/bc537) [@dilhanm](https://github.com/DilhanM) ·