A quantum machine learning classifier to search for new physics

TL;DR

This paper introduces quantum machine learning algorithms, QSN and VQSN, for searching for new physics signals in high-energy collider data, demonstrating their efficiency and robustness on real quantum hardware.

Contribution

It proposes novel quantum algorithms for particle physics data analysis and validates their performance on real quantum hardware, advancing quantum applications in high-energy physics.

Findings

VQSN outperforms classical k-nearest neighbor in efficiency

Algorithms successfully tested on real quantum hardware

Reliable performance under noisy conditions achieved

Abstract

Due to the success of the Standard Model~(SM), it is reasonable to anticipate that the signal of new physics~(NP) beyond the SM is small. Consequently, future searches for NP and precision tests of the SM will require high luminosity collider experiments. Moreover, as precision tests advance, rare processes with many final-state particles require consideration which demands the analysis of a vast number of observables. The high luminosity produces a large amount of experimental data spanning a large observable space, posing a significant data-processing challenge. In recent years, quantum machine learning has emerged as a promising approach for processing large amounts of complex data on a quantum computer. In this study, we propose quantum searching neighbor~(QSN) and variational QSN~(VQSN) algorithms to search for NP. The QSN is a classification algorithm. The VQSN introduces variation to the QSN to process classical data. As applications, we apply the (V)QSN in the phenomenological study of the NP at the Large Hadron Collider and muon colliders. Examples are implemented on a real quantum hardware, which confirms reliable performance under noisy conditions. The results indicate that the VQSN demonstrates superior efficiency in the sense of computational complexity to a classical counterpart k-nearest neighbor algorithm, even when dealing with classical data.