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Computer Physics Communications

MPS-VMC: A high-performance matrix product state variational Monte Carlo Solver for ab initio quantum chemistry

Zhiqian Xu, Yangjun Wu, Lizhong Fu, Chu Guo, Honghui Shang

Published Jan 31, 2026• 1 citation
Publisher
DOI
10.1016/j.cpc.2026.110057

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Abstract Neural-network quantum states (NQSs) offer a powerful and expressive ansatz for representing quantum many-body wave functions. However, their training via Variational Monte Carlo (VMC) methods remains challenging. It is well known that some scenarios—such as sharply peaked wave functions emerging in quantum chemistry—lead to high-variance gradient estimators hindering the effectiveness of variational optimizations. In this work we investigate a systematic strategy to tackle those sampling issues by means of adaptively tuned importance sampling. Our approach is explicitly designed to target the gradient estimator instead of the loss function, and be computationally inexpensive. We benchmarked our approach across the ground-state search of a wide variety of hamiltonians, including frustrated spin systems and ab-initio quantum chemistry. Overall, our approach can reduce the computational cost of vanilla VMC considerably, up to a factor of 100× when targeting highly peaked quantum chemistry wavefunctions.
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