Accelerating Quantum-Classical Auxiliary-Field Quantum Monte Carlo with Shadow-Trained Neural Quantum States
Abstract
Quantum-classical auxiliary-field quantum Monte Carlo (QC-AFQMC) uses a trial state prepared on a quantum computer to improve the accuracy of the classical AFQMC algorithm. State-of-the-art QC-AFQMC represents this state with matchgate shadows, so every trial-walker overlap and local energy must be averaged over many snapshots, making this classical computation a bottleneck. Here we distill the matchgate shadow data into a symmetry-constrained, noise-aware, autoregressive transformer neural quantum state (NQS). Used as the AFQMC trial state, the NQS evaluates overlaps, force biases, and local energies without repeatedly summing over the matchgate shadows. For a nickel-catalyzed oxidative-addition reaction, the noise-awareness of our NQS design successfully recovers the ideal-upCCD AFQMC barrier and reaction energy within statistical uncertainty, while standard QC-AFQMC execution with the same shadow data yields qualitatively inaccurate results as a result of hardware noise. Since the shadow data are needed only for training, the estimated classical cost for the nickel system falls by approximately 99% relative to our previous QC-AFQMC implementation.
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BibTeX
@misc{provazza2026accelerating,
title = {{Accelerating Quantum-Classical Auxiliary-Field Quantum Monte Carlo with Shadow-Trained Neural Quantum States}},
author = {Justin Provazza and Oliver Knitter and Joshua Goings and Evgeny Epifanovsky and Chi Chen and Martin Roetteler and Luning Zhao},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.08129},
eprint = {2611.08129},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.08129},
}
RIS
TY - UNPB
TI - Accelerating Quantum-Classical Auxiliary-Field Quantum Monte Carlo with Shadow-Trained Neural Quantum States
AU - Justin Provazza
AU - Oliver Knitter
AU - Joshua Goings
AU - Evgeny Epifanovsky
AU - Chi Chen
AU - Martin Roetteler
AU - Luning Zhao
PY - 2026
DA - 2026/11/05
PB - arXiv
DO - 10.48550/arxiv.2611.08129
UR - https://arxiv.org/abs/2611.08129
AB - Quantum-classical auxiliary-field quantum Monte Carlo (QC-AFQMC) uses a trial state prepared on a quantum computer to improve the accuracy of the classical AFQMC algorithm. State-of-the-art QC-AFQMC represents this state with matchgate shadows, so every trial-walker overlap and local energy must be averaged over many snapshots, making this classical computation a bottleneck. Here we distill the matchgate shadow data into a symmetry-constrained, noise-aware, autoregressive transformer neural quantum state (NQS). Used as the AFQMC trial state, the NQS evaluates overlaps, force biases, and local energies without repeatedly summing over the matchgate shadows. For a nickel-catalyzed oxidative-addition reaction, the noise-awareness of our NQS design successfully recovers the ideal-upCCD AFQMC barrier and reaction energy within statistical uncertainty, while standard QC-AFQMC execution with the same shadow data yields qualitatively inaccurate results as a result of hardware noise. Since the shadow data are needed only for training, the estimated classical cost for the nickel system falls by approximately 99% relative to our previous QC-AFQMC implementation.
ER -