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arXiv:2506.09611 · Physical review. B./Physical review. B

Accelerating ground-state auxiliary-field quantum Monte Carlo simulations by delayed update and block force-bias update

Hao Du, Yuan-Yao He

Published Nov 21, 2025• 3 citations
arXivHTMLPDF

Abstract

Ground-state auxiliary-field quantum Monte Carlo is a powerful tool for interacting fermions but is often limited by the bottleneck of costly local update. Here, the authors develop two acceleration schemes: the delayed update, which replaces multiple vector outer products by one matrix-matrix multiplication, and the block force-bias update, which retains high efficiency while tuning acceptance via block size. Applied to 2D Hubbard models, they yield up to ~ 8 times speedup in 1600-site systems. These update schemes can have broad applicability to general correlated fermion systems.

DOI
10.1103/mnp6-mzfj

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Ground-state auxiliary-field quantum Monte Carlo is a powerful tool for interacting fermions but is often limited by the bottleneck of costly local update. Here, the authors develop two acceleration schemes: the delayed update, which replaces multiple vector outer products by one matrix-matrix multiplication, and the block force-bias update, which retains high efficiency while tuning acceptance via block size. Applied to 2D Hubbard models, they yield up to ~ 8 times speedup in 1600-site systems. These update schemes can have broad applicability to general correlated fermion systems.
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In this work, we report, for the first time, an implementation of fermionic auxiliary-field quantum Monte Carlo (AFQMC) using matrix product state (MPS) trial wavefunctions, dubbed MPS-AFQMC. Calculating overlaps between an MPS trial and arbitrary Slater determinants up to a multiplicative error, a crucial subroutine in MPS-AFQMC, is proven to be #P-hard. Nonetheless, we tested several promising heuristics in successfully improving fermionic phaseless AFQMC energies. We also proposed a way to evaluate local energy and force bias evaluations free of matrix product operators. This allows for larger basis set calculations without significant overhead. We showcase the utility of our approach on one- and two-dimensional hydrogen lattices, even when the MPS trial itself struggles to obtain high accuracy. Our work offers a new set of tools that can solve currently challenging electronic structure problems with future improvements.
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This paper presents an efficient algorithm for computing the transition probability in auxiliary field quantum Monte Carlo simulations of strongly correlated electron systems using a Hubbard model. This algorithm is based on a low rank updating of the underlying linear algebra problem, and results in significant computational savings. The computational complexity of computing the transition probability and Green's function update reduces to O(k2)\mathcal{O}({k}^{2})O(k2) during the kthk\text{th}kth step, where kkk is the number of accepted spin flips, and results in an algorithm that is faster than the competing delayed update algorithm. Moreover, this algorithm is orders of magnitude faster than traditional algorithms that use naive updating of the Green's function matrix.
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We propose novel Quantum Monte Carlo (QMC) methods for interacting many-fermion systems by leveraging the stochastic gauge freedom, originally developed in Gaussian phase-space QMC, within the phaseless auxiliary-field QMC (AFQMC) framework. In particular, we reinterpret the conventional force bias in phaseless AFQMC as a drift gauge and explore Fermi gauges based on natural orbitals of a reduced one-body density matrix defined via a mixed estimator, yielding stochastic, time-dependent Hartree-Fock-like dynamics. We propose a symmetry-projection sampling scheme to enhance the sampling efficiency. As a proof of concept, we apply these gauge-augmented AFQMC methods to a simple shell-model Hamiltonian: the Lipkin-Meshkov-Glick model. Numerical results illustrate the potential of stochastic gauges to enhance accuracy and reduce fluctuations, underscoring the promise for advancing these new techniques toward more realistic shell-model applications.