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  • Open Access

One particle - one qubit: Particle physics data encoding for quantum machine learning

Aritra Bal* and Markus Klute

Benedikt Maier†, Melik Oughton, and Eric Pezone

Michael Spannowsky‡

  • Institute for Particle Physics Phenomenology (IPPP), Durham University, Durham, United Kingdom

  • *Contact author: aritra.bal@kit.edu
  • †Contact author: benedikt.maier@cern.ch
  • ‡Contact author: michael.spannowsky@durham.ac.uk
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Phys. Rev. D 112, 076004 – Published 6 October, 2025

DOI: https://doi.org/10.1103/l8y2-87vq

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 on quantum circuits 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 quantum chromodynamics (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.

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Outline

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Phys. Rev. D 112, 076004– Published 6 October, 2025
  • Received 2 March 2025
  • Accepted 20 August 2025
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