Machine learning quantum data to accelerate quantum Monte Carlo
Abstract
Simulating strongly correlated electrons in molecules and materials remains a central goal for quantum computing. A promising strategy uses quantum processors to prepare trial wavefunctions that guide classical quantum Monte Carlo (QMC). However, the enormous computational cost of repeatedly processing quantum measurements has severely limited the practical scalability of this approach. Here, we overcome this bottleneck by compressing quantum measurement data into machine-learning models that efficiently provide the wavefunction overlaps needed by QMC. This accelerates quantum-classical QMC by orders of magnitude, enabling chemically accurate simulations on up to 40 qubits, more than doubling the scale of previous hardware demonstrations with comparable accuracy. We demonstrate our approach on hydrogen chains and strongly correlated iron-sulfur clusters using measurements from Google's Willow processor. By enabling efficient reuse of quantum-state information on the classical coprocessor, our framework addresses a fundamental bottleneck in hybrid quantum algorithms that will persist even into the fault-tolerant era.
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BibTeX
@misc{jiang2026machine,
title = {{Machine learning quantum data to accelerate quantum Monte Carlo}},
author = {Tong Jiang and Andrew Zhao and Shashwat Kumar and Jigyasa Nigam and Eliott Rosenberg and Ryan Babbush and Andrew D. Baczewski and William J. Huggins and Joonho Lee},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.08151},
eprint = {2611.08151},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.08151},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Machine learning quantum data to accelerate quantum Monte Carlo
AU - Tong Jiang
AU - Andrew Zhao
AU - Shashwat Kumar
AU - Jigyasa Nigam
AU - Eliott Rosenberg
AU - Ryan Babbush
AU - Andrew D. Baczewski
AU - William J. Huggins
AU - Joonho Lee
PY - 2026
DA - 2026/11/05
PB - arXiv
DO - 10.48550/arxiv.2611.08151
UR - https://arxiv.org/abs/2611.08151
AB - Simulating strongly correlated electrons in molecules and materials remains a central goal for quantum computing. A promising strategy uses quantum processors to prepare trial wavefunctions that guide classical quantum Monte Carlo (QMC). However, the enormous computational cost of repeatedly processing quantum measurements has severely limited the practical scalability of this approach. Here, we overcome this bottleneck by compressing quantum measurement data into machine-learning models that efficiently provide the wavefunction overlaps needed by QMC. This accelerates quantum-classical QMC by orders of magnitude, enabling chemically accurate simulations on up to 40 qubits, more than doubling the scale of previous hardware demonstrations with comparable accuracy. We demonstrate our approach on hydrogen chains and strongly correlated iron-sulfur clusters using measurements from Google's Willow processor. By enabling efficient reuse of quantum-state information on the classical coprocessor, our framework addresses a fundamental bottleneck in hybrid quantum algorithms that will persist even into the fault-tolerant era.
ER -