1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning

TL;DR

This paper introduces a novel quantum data encoding scheme for high-energy physics that assigns each particle to a qubit, enabling efficient quantum machine learning applications like anomaly detection and jet classification, with promising results on real data.

Contribution

The paper presents the 1P1Q encoding scheme, a new method for representing collision events directly in quantum states, improving quantum ML performance and scalability in HEP analysis.

Findings

Quantum autoencoder effectively detects anomalies in collider data.

Variational quantum circuit achieves competitive classification accuracy.

Validation on real CMS data confirms practical robustness.

Abstract

We introduce 1P1Q, a novel quantum data encoding scheme for high-energy physics (HEP), where each particle is assigned to an individual qubit, enabling direct representation of collision events without classical compression. We demonstrate the effectiveness of 1P1Q in quantum machine learning (QML) through two applications: a Quantum Autoencoder (QAE) for unsupervised anomaly detection and a Variational Quantum Circuit (VQC) for supervised classification of top quark jets. Our results show that the QAE successfully distinguishes signal jets from background QCD jets, achieving superior performance compared to a classical autoencoder while utilizing significantly fewer trainable parameters. Similarly, the VQC achieves competitive classification performance, approaching state-of-the-art classical models despite its minimal computational complexity. Furthermore, we validate the QAE on real experimental data from the CMS detector, establishing the robustness of quantum algorithms in practical HEP applications. These results demonstrate that 1P1Q provides an effective and scalable quantum encoding strategy, offering new opportunities for applying quantum computing algorithms in collider data analysis.