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

@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 and Python 3 installed on your computer. From your command line:

# 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 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.

Related

Check out 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.

License

This project uses the Apache License 2.0.

hartree.stfc.ac.uk  ·  ch.cam.ac.uk  ·  GitHub @stfc @edoaltamura @bc537 @dilhanm  ·