https://github.com/cern-it-innovation/latent-ad-qml

Unsupervised anomaly detection in the latent space of high energy physics events with quantum machine learning.

https://github.com/cern-it-innovation/latent-ad-qml

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Unsupervised anomaly detection in the latent space of high energy physics events with quantum machine learning.

https://github.com/CERN-IT-INNOVATION/latent-ad-qml/blob/main/

# Quantum anomaly detection in the latent space of proton collision events at the LHC
   	
[![DOI](https://zenodo.org/badge/494404586.svg)](https://zenodo.org/badge/latestdoi/494404586)
[![DOI:10.48550/arXiv.2301.10780](http://img.shields.io/badge/DOI-10.48550/arXiv.2301.10780-B31B1B.svg)](https://doi.org/10.48550/arXiv.2301.10780)
[![Email: vasilis](https://img.shields.io/badge/email-vasileios.belis%40cern.ch-blue?style=flat-square&logo=minutemailer)](mailto:vasileios.belis@cern.ch)
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This repository has the code we developed for the paper _"Quantum anomaly detection in the latent space of proton collision events at the LHC"_ [[1]](https://arxiv.org/abs/2301.10780). In this work, we investigate unsupervised quantum machine learning algorithms for anomaly detection tasks in particle physics data. 

The `qad` package associated with this work was created for reproducibility of the results and ease-of-use in future studies.




The figure above, taken from [[1]](https://arxiv.org/abs/2301.10780), depicts the _quantum\-classical pipeline_ for detecting (anomalous) new-physics events in proton collisions at the LHC. Our strategy, implemented in `qad`, combines a data compression scheme with unsupervised quantum machine learning models to assist in scientific discovery at high energy physics experiments.

## Documentation 
The documentation can be consulted in the readthedocs [page](https://latent-ad-qml.readthedocs.io/en/latest/).

## Citation
Please cite our work if you found it useful in your own research.
```
@article{wozniak_belis_puljak2023,
  doi = {10.48550/ARXIV.2301.10780},
  url = {https://arxiv.org/abs/2301.10780},
  author = {Woniak, Kinga Anna and Belis, Vasilis and Puljak, Ema and Barkoutsos, Panagiotis and Dissertori, Gnther and Grossi, Michele and Pierini, Maurizio and Reiter, Florentin and Tavernelli, Ivano and Vallecorsa, Sofia},
  keywords = {Quantum Physics (quant-ph), Machine Learning (cs.LG), High Energy Physics - Experiment (hep-ex), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Quantum anomaly detection in the latent space of proton collision events at the LHC},
  publisher = {arXiv},
  year = {2023},
  copyright = {Creative Commons Attribution 4.0 International}
}
```

## How to install
The package can be installed with Python's `pip` package manager. We recommend installing the dependencies and the package within a dedicated environment. 
You can directly install `qad` by running:

```
pip install https://github.com/vbelis/latent-ad-qml/archive/main.zip
```
or by first cloning the repo locally and then installing the package:
```bash
git clone https://github.com/vbelis/latent-ad-qml.git
cd latent-ad-qml
pip install .
```

## Usage
Examples on how to run the code and use `qad` to reproduce results and plots from the paper can be found in the [scripts](https://github.com/vbelis/latent-ad-qml/tree/main/scripts). Check also the corresponding documentation page.


# References
[1] K. A. Woniak\*, V. Belis\*, E. Puljak\*, P. Barkoutsos, G. Dissertori, M. Grossi, M. Pierini, F. Reiter, I. Tavernelli, S. Vallecorsa , _Quantum anomaly detection in the latent space of proton collision events at the LHC_, [arXiv:2301.10780](https://arxiv.org/abs/2301.10780). 

\* equal contribution

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