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arXiv Machine Learning By Tariq Mahmood, Zain ul Abidin, Itzel Luviano Soto, Alfredo Raya

Comparing Classical and Quantum Machine Learning for Regression in High Energy Physics Collision Data

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The paper systematically compares four classical machine learning models—SVM, ANN, CNN, and LSTM—with their quantum equivalents—QSVM, QNN, QCNN, and QLSTM—on simulated proton‑proton collision data from CERN Open Data. Classical models, especially CNN and LSTM, slightly outperform the quantum models under current hardware and dataset constraints, but quantum models achieve comparable accuracy with far fewer trainable parameters; for example, the QCNN matches a deep classical CNN using only four qubits and a depth‑three circuit. The study also shows that the regression task is non‑trivial for shallow polynomial fits, underscoring the relevance of the architectural comparison. whyItMatters":"The work provides a realistic benchmark of classical versus quantum machine learning performance on high‑energy physics data, highlighting parameter‑efficiency advantages of quantum models for near‑term devices."

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