Anomaly detection with quantum machine learning for particle physics data
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]. 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], 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.
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:
#!/bin/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.
References
[1] K. A. Woźniak, 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.
Structure
Dimensionality Reduction
Algorithms
Analysis