Evaluating Quantum Kernel Methods for Track-Based Classification in High-Energy Physics

• URL: http://arxiv.org/abs/2608.27333v1
• Date: Thu, 27 Aug 2026 16:35:30 GMT
• Title: Evaluating Quantum Kernel Methods for Track-Based Classification in High-Energy Physics
• Abstract summary: We present a design for large-scale quantum kernel classification using centroid-based CLAS12 drift-chamber features.<n>Each event is encoded into a six-qubit state via a fully entangled ZZFeatureMap, whose fidelities define a quantum kernel within a standard SVM framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a systematic design for large-scale quantum kernel classification, demonstrated through a quantum support vector classifier (QSVC) for particle-track classification using centroid-based CLAS12 drift-chamber features. Each event is encoded into a six-qubit state via a fully entangled ZZFeatureMap, whose fidelities define a quantum kernel within a standard SVM framework. By decoupling state preparation from kernel construction and distributing evaluation across a multi-node MPI-based HPC allocation, the approach scales to 1.0x10^5 training and 4.0x10^5 test events with an exactly constructed kernel matrix, to our knowledge more than an order of magnitude larger than prior high-energy-physics quantum-kernel studies. Benchmarked against linear, polynomial, RBF, and sigmoid SVM kernels and extremely randomized trees (ERT), the ideal QSVC achieves the highest recall (99.99%) among all models. Under a calibrated hardware noise model (FakeMumbaiV2, 500 training / 2,000 test events), AUC falls from 0.9985 to 0.9671 and peak significance improvement falls from 17.5 to ~3.5, yet recall remains at 99.51% -- indicating this signal-retention advantage is attenuated but not eliminated by circuit-level decoherence. Geometric analysis of the quantum embedding shows near-orthogonal inter-class states with coherent intra-class neighborhoods under ideal simulation; under noise this structure compresses toward the maximally mixed state while preserving its relative ordering. These results demonstrate a scalable, reproducible workflow for quantum kernel experimentation at HEP-relevant scale, quantifying the practical cost of realistic hardware noise on quantum-enhanced classification.

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