Quantum Machine Learning for particle scattering entanglement classification

TL;DR

This paper explores using quantum machine learning, specifically QCNNs, to classify entanglement in particle scattering processes via fermion density profiles, showing quantum models can outperform classical ones.

Contribution

It demonstrates that compact QCNNs can effectively classify entanglement thresholds using accessible observables, highlighting the importance of encoding and model size over scaling.

Findings

QCNNs outperform classical CNNs in accuracy and convergence speed

Larger models do not necessarily improve performance and are more sensitive to encoding

A 4-qubit QCNN achieves optimal results with better trainability

Abstract

Entanglement is a key quantity for characterizing quantum correlations in particle scattering processes, but its direct evaluation is computationally demanding on quantum hardware. In this work, we investigate whether fermion density profiles, which are easier to access, can serve as proxies for entanglement by framing the problem as a classification task across multiple entanglement thresholds. Using the fermion scattering in the Thirring model as a test bed, we compare Quantum Convolutional Neural Networks (QCNNs) with classical CNNs of comparable parameter counts, and find that QCNNs achieve consistently competitive or superior accuracy with faster convergence and lower variance. Notably, we observe that increasing the model size does not improve the performance within the architectures studied here, and larger models appear to be more sensitive to the choice of encoding. Instead, a compact 4-qubits QCNN provides the best results, suggesting the importance of trainability and encoding choices over model scaling. These findings demonstrate the potential of quantum and quantum-inspired machine learning models for extracting nontrivial quantum information from accessible observables, with implications for high-energy physics and quantum many-body systems.