Unsupervised Beyond-Standard-Model Event Discovery at the LHC with a Novel Quantum Autoencoder

TL;DR

This paper introduces a novel quantum autoencoder for unsupervised anomaly detection at the LHC, demonstrating superior performance over classical methods and analyzing quantum properties like entanglement and magic during training.

Contribution

It presents a new quantum autoencoder architecture tailored for particle physics anomaly detection and compares its effectiveness with classical autoencoders, revealing insights into quantum circuit properties.

Findings

Quantum autoencoder outperforms classical autoencoders in detecting new physics signals.

Both entanglement and magic metrics decrease during training, correlating with loss reduction.

Quantum autoencoder achieves high performance with fewer trainable parameters.

Abstract

This study explores the potential of unsupervised anomaly detection for identifying physics beyond the Standard Model that may appear at proton collisions at the Large Hadron Collider. We introduce a novel quantum autoencoder circuit ansatz that is specifically designed for this task and demonstrates superior performance compared to previous approaches. To assess its robustness, we evaluate the quantum autoencoder on various types of new physics 'signal' events and varying problem sizes. Additionally, we develop classical autoencoders that outperform previously proposed quantum autoencoders but remain outpaced by the new quantum ansatz, despite its significantly reduced number of trainable parameters. Finally, we investigate the properties of quantum autoencoder circuits, focusing on entanglement and magic. We introduce a novel metric in the context of parameterised quantum circuits, stabilizer 2-R\'enyi entropy to quantify magic, along with the previously studied Meyer-Wallach measure for entanglement. Intriguingly, both metrics decreased throughout the training process along with the decrease in the loss function. This appears to suggest that models preferentially learn parameters that reduce these metrics. This study highlights the potential utility of quantum autoencoders in searching for physics beyond the Standard Model at the Large Hadron Collider and opens exciting avenues for further research into the role of entanglement and magic in quantum machine learning more generally.