Quantum ML models show parameter efficiency in physics data regression

Researchers have systematically compared classical machine learning models like CNNs and LSTMs against their quantum counterparts (QCNN, QLSTM) for regression tasks in high energy physics collision data. While classical models currently achieve slightly better performance, quantum models demonstrate a significant advantage in parameter efficiency, with a QCNN requiring far fewer parameters than a deep classical CNN to reach comparable accuracy. This study provides a benchmark for future research on actual quantum hardware, highlighting the trade-offs under resource-constrained conditions. AI

RANK_REASON Academic paper comparing classical and quantum machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

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• artificial neural network
• CERN
• CNN
• LSTM
• QCNN
• QLSTM
• QNN
• QSVM
• support vector machine

• paper
• other

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