Quantum similarity learning for anomaly detection

TL;DR

This paper investigates the use of quantum computing to enhance similarity learning for anomaly detection in high-energy physics data, demonstrating potential improvements with hybrid quantum-classical models and noise reduction techniques.

Contribution

It introduces a hybrid classical-quantum similarity learning approach for anomaly detection in LHC data, showing improved performance over classical methods in noisy quantum environments.

Findings

Hybrid quantum-classical network outperforms classical similarity learning in noise-free scenarios.

Measurement noise reduction via clustering yields a 9% performance improvement.

Quantum algorithms show promise for future LHC data analysis with fault-tolerant quantum computers.

Abstract

Anomaly detection is a vital technique for exploring signatures of new physics Beyond the Standard Model (BSM) at the Large Hadron Collider (LHC). The vast number of collisions generated by the LHC demands sophisticated deep learning techniques. Similarity learning, a self-supervised machine learning, detects anomalous signals by estimating their similarity to background events. In this paper, we explore the potential of quantum computers for anomaly detection through similarity learning, leveraging the power of quantum computing to enhance the known similarity learning method. In the realm of noisy intermediate-scale quantum (NISQ) devices, we employ a hybrid classical-quantum network to search for heavy scalar resonances in the di-Higgs production channel. In the absence of quantum noise, the hybrid network demonstrates improvement over the known similarity learning method. Moreover, we employ a clustering algorithm to reduce measurement noise from limited shot counts, resulting in $9\%$ improvement in the hybrid network performance. Our analysis highlights the applicability of quantum algorithms for LHC data analysis, where improvements are anticipated with the advent of fault-tolerant quantum computers.