Anomaly detection in high-energy physics using a quantum autoencoder

TL;DR

This paper demonstrates that quantum autoencoders can effectively detect anomalies in high-energy physics data, outperforming classical autoencoders and being feasible on current quantum hardware, thus promising for future LHC analyses.

Contribution

The study introduces quantum autoencoders for anomaly detection in high-energy physics, showing their superior performance and practical feasibility compared to classical methods.

Findings

Quantum autoencoders outperform classical autoencoders in anomaly detection.

Quantum autoencoders train efficiently and are reproducible on current quantum devices.

Potential for quantum autoencoders to analyze future LHC data effectively.

Abstract

The lack of evidence for new interactions and particles at the Large Hadron Collider has motivated the high-energy physics community to explore model-agnostic data-analysis approaches to search for new physics. Autoencoders are unsupervised machine learning models based on artificial neural networks, capable of learning background distributions. We study quantum autoencoders based on variational quantum circuits for the problem of anomaly detection at the LHC. For a QCD $t\bar{t}$ background and resonant heavy Higgs signals, we find that a simple quantum autoencoder outperforms classical autoencoders for the same inputs and trains very efficiently. Moreover, this performance is reproducible on present quantum devices. This shows that quantum autoencoders are good candidates for analysing high-energy physics data in future LHC runs.