https://github.com/vbelis/latent-ad-qml
Unsupervised anomaly detection in the latent space of high energy physics events with quantum machine learning.
https://github.com/vbelis/latent-ad-qml
anomaly-detection deep-learning particle-physics quantum-machine-learning unsupervised-learning
Last synced: 5 months ago
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Unsupervised anomaly detection in the latent space of high energy physics events with quantum machine learning.
- Host: GitHub
- URL: https://github.com/vbelis/latent-ad-qml
- Owner: vbelis
- License: mit
- Created: 2022-05-20T09:37:16.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-06-07T13:56:10.000Z (about 2 years ago)
- Last Synced: 2024-06-07T15:24:45.462Z (about 2 years ago)
- Topics: anomaly-detection, deep-learning, particle-physics, quantum-machine-learning, unsupervised-learning
- Language: Python
- Homepage:
- Size: 3.06 MB
- Stars: 7
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Quantum anomaly detection in the latent space of proton collision events at the LHC
[](https://www.nature.com/articles/s42005-024-01811-6)
[](https://zenodo.org/badge/latestdoi/494404586)
[](mailto:vasileios.belis@cern.ch)
[](https://quantum.cern/our-governance)
[](https://github.com/psf/black)
[](https://www.python.org/downloads/)
[](https://github.com/QML-HEP/ae_qml/blob/main/LICENSE)
[](https://latent-ad-qml.readthedocs.io/en/latest/?badge=latest)
This repository has the code we developed for the paper:
Belis, V., Woźniak, K.A., Puljak, E. et al. Quantum anomaly detection in the latent space of proton collision events at the LHC. _Nature Communications Physics_ **7**, 334 (2024). https://doi.org/10.1038/s42005-024-01811-6
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 the paper, 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{Belis_2024,
title={Quantum anomaly detection in the latent space of proton collision events at the LHC},
volume={7},
ISSN={2399-3650},
DOI={10.1038/s42005-024-01811-6},
number={1},
journal={Communications Physics},
author={Belis, Vasilis and Woźniak, Kinga Anna and Puljak, Ema and Barkoutsos, Panagiotis and Dissertori, Günther and Grossi, Michele and Pierini, Maurizio and Reiter, Florentin and Tavernelli, Ivano and Vallecorsa, Sofia}, year={2024},
month=oct,
pages={334}
}
```
## 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. For instance, we use `conda` to create a `python` environment:
```
conda create -n my_env python=3.8
conda activate my_env
```
In this 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 .
```
The installation is expected to take a couple of minutes.
## 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.