Benchmarking Quantum Convolutional Neural Networks and their classical surrogates on long-range strongly correlated models
Abstract
We benchmark quantum convolutional neural networks (QCNNs) and their classical surrogates for phase differentiation in one-dimensional strongly correlated systems with beyond-nearest-neighbour terms. Using an anisotropic Heisenberg spin chain and an extended t-t' Fermi-Hubbard model as representative spin and fermionic testbeds, we train QCNN classifiers on labelled ground-state datasets and map out finite-size phase diagrams. In hybrid setups, we employ Pauli backpropagation to express the QCNN measurement as a weighted sum of Pauli-string expectation values to be evaluated directly on input states, and study how truncation parameters control the trade-off between approximation accuracy and the size of the resulting Pauli expansion. Robust convergence is achieved for the Heisenberg model under aggressive truncation, whereas the Fermi-Hubbard model requires substantially looser thresholds, resulting in a sharp growth in retained Pauli strings and evaluation cost. Our results contribute to charting where hybrid quantum-classical training is viable for learning strongly correlated quantum states, and provide practical guidance for choosing truncation regimes in Pauli backpropagation-based surrogates.
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BibTeX
@misc{bespalova2026benchmarking,
title = {{Benchmarking Quantum Convolutional Neural Networks and their classical surrogates on long-range strongly correlated models}},
author = {Tatiana A. Bespalova and Réka Schwengelbeck and Laura Mismetti and Johannes Schachenmayer and Ivano Tavernelli and Francesco Tacchino},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00255},
eprint = {2611.00255},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00255},
}
RIS
TY - UNPB
TI - Benchmarking Quantum Convolutional Neural Networks and their classical surrogates on long-range strongly correlated models
AU - Tatiana A. Bespalova
AU - Réka Schwengelbeck
AU - Laura Mismetti
AU - Johannes Schachenmayer
AU - Ivano Tavernelli
AU - Francesco Tacchino
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00255
UR - https://arxiv.org/abs/2611.00255
AB - We benchmark quantum convolutional neural networks (QCNNs) and their classical surrogates for phase differentiation in one-dimensional strongly correlated systems with beyond-nearest-neighbour terms. Using an anisotropic Heisenberg spin chain and an extended t-t' Fermi-Hubbard model as representative spin and fermionic testbeds, we train QCNN classifiers on labelled ground-state datasets and map out finite-size phase diagrams. In hybrid setups, we employ Pauli backpropagation to express the QCNN measurement as a weighted sum of Pauli-string expectation values to be evaluated directly on input states, and study how truncation parameters control the trade-off between approximation accuracy and the size of the resulting Pauli expansion. Robust convergence is achieved for the Heisenberg model under aggressive truncation, whereas the Fermi-Hubbard model requires substantially looser thresholds, resulting in a sharp growth in retained Pauli strings and evaluation cost. Our results contribute to charting where hybrid quantum-classical training is viable for learning strongly correlated quantum states, and provide practical guidance for choosing truncation regimes in Pauli backpropagation-based surrogates.
ER -