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arXiv Machine Learning By Tariq Mahmood, Muhammad Awais Rafique, Talab Hussain, Juan Pablo Perez Aguilar, Alfredo Raya, Muhammad Ahsan

Classical and Hybrid Quantum Machine Learning for Trigger-Like Event Selection on CMS Open Data: An Eight-Qubit, PCA-Constrained Benchmark

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The paper compares classical and hybrid quantum machine learning models for a trigger-like binary classification task using CMS open data. Eight classical models (SVM, ANN, CNN, LSTM) and eight quantum counterparts are evaluated under identical preprocessing, data splits, and decision thresholds, with performance measured by accuracy, ROC‑AUC, F1‑score, precision, and recall. The best classical model is an artificial neural network (93.53 % accuracy, 0.9819 ROC‑AUC), while the best quantum model is a quantum convolutional network (90.89 % accuracy, 0.9731 ROC‑AUC), indicating that within an eight‑qubit budget the quantum models do not surpass the classical ones.

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