A comparative study of variational quantum classifiers and classical machine learning for tW−tt̅ discrimination at the LHC
Abstract
Precise measurements of single top-quark production in the associated tW-channel require efficient discrimination against the dominant tt̅ background. In this work, we investigate the applicability of quantum machine learning to this classification problem using the Variational Quantum Classifier (VQC) algorithm. Simulated proton-proton collision events from CMS Open Data corresponding to Run-2 are employed to train and evaluate the VQC model. Its performance is compared with two established classical approaches, Boosted Decision Trees (BDTs) and Multi-Layer Perceptron (MLP) using both machine learning metrics and physics-motivated metrics that describe the Signal Significance as a function of Signal Efficiency. The comparison is performed separately for the dileptonic e−μ channel and for the resolved and boosted hadronic topologies. In addition, signal-enriched and background-enriched phase-space regions are studied to assess the dependence of classifier performance on event topology. Our results provide a systematic benchmark for the application of VQC for a realistic High Energy Physics classification task and identify the analysis regimes wherein quantum-inspired models offer competitive discrimination with respect to established classical techniques.
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BibTeX
@misc{hindocha2026comparative,
title = {{A comparative study of variational quantum classifiers and classical machine learning for $tW-t\bart$ discrimination at the LHC}},
author = {Rohan Hindocha and Priyanka},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.22504},
eprint = {2610.22504},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.22504},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A comparative study of variational quantum classifiers and classical machine learning for tW−tt̅ discrimination at the LHC
AU - Rohan Hindocha
AU - Priyanka
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.22504
UR - https://arxiv.org/abs/2610.22504
AB - Precise measurements of single top-quark production in the associated tW-channel require efficient discrimination against the dominant tt̅ background. In this work, we investigate the applicability of quantum machine learning to this classification problem using the Variational Quantum Classifier (VQC) algorithm. Simulated proton-proton collision events from CMS Open Data corresponding to Run-2 are employed to train and evaluate the VQC model. Its performance is compared with two established classical approaches, Boosted Decision Trees (BDTs) and Multi-Layer Perceptron (MLP) using both machine learning metrics and physics-motivated metrics that describe the Signal Significance as a function of Signal Efficiency. The comparison is performed separately for the dileptonic e−μ channel and for the resolved and boosted hadronic topologies. In addition, signal-enriched and background-enriched phase-space regions are studied to assess the dependence of classifier performance on event topology. Our results provide a systematic benchmark for the application of VQC for a realistic High Energy Physics classification task and identify the analysis regimes wherein quantum-inspired models offer competitive discrimination with respect to established classical techniques.
ER -