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

A new arXiv preprint (2608.28084v1) systematically compares classical and quantum machine learning models for regression on simulated proton-proton collision data from the CERN Open Data portal, finding that classical CNN and LSTM architectures achieve marginally better quantitative performance, while the quantum CNN (QCNN) matches the deep classical CNN using only four qubits and a circuit depth of three, demonstrating a parameter-efficiency advantage on near-term quantum devices.

arXiv:2608.28084v1 Announce Type: new Abstract: The classification and regression of particle collision events constitute a persistent computational challenge in experimental high energy physics, where large volumes of simulated data must be processed with both speed and precision. This work carries out a systematic comparison of four classical machine learning architectures, support vector machines SVM , artificial neural networks ANN , convolutional neural networks CNN , and long short-term memory LSTM networks against their quantum counterparts: quantum SVM QSVM , quantum neural networks QNN , quantum CNN QCNN , and quantum LSTM QLSTM . All models are trained on simulated proton-proton collision events with electron-positron and muon-antimuon final states from the CERN Open Data portal, using transverse-momentum components as input features and transverse-momentum magnitude as the regression target. Classical architectures, and in particular the CNN and LSTM, achieve marginally better quantitative performance under current hardware and dataset constraints. Quantum models, however, reach competitive accuracy with substantially fewer trainable parameters: the QCNN reproduces the performance of the deep classical CNN using only four qubits and a circuit of depth three, pointing to a genuine parameter-efficiency advantage on near-term quantum devices. A baseline analysis confirms that the regression problem is non-trivial for shallow polynomial fits, supporting the relevance of the architectural comparison. These results characterize the trade-offs between classical and quantum approaches under realistic, resource-constrained conditions and provide a benchmark for future studies on actual quantum hardware.