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The auxiliary-field quantum Monte Carlo (AFQMC) method is a general numerical method for correlated many-electron systems, which is being increasingly applied in lattice models, atoms, molecules, and solids. Here we introduce the theory and…

Computational Physics · Physics 2021-02-24 Hao Shi , Shiwei Zhang

QMCPACK has enabled cutting-edge materials research on supercomputers for over a decade. It scales nearly ideally but has low single-node efficiency due to the physics-based abstractions using array-of-structures objects, causing…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-08-10 Amrita Mathuriya , Ye Luo , Raymond C. Clay , Anouar Benali , Luke Shulenburger , Jeongnim Kim

Using trial wavefunctions prepared on quantum devices to reduce the bias of auxiliary-field quantum Monte Carlo (QC-AFQMC) has established itself as a promising hybrid approach to the simulation of strongly correlated many body systems.…

Quantum Physics · Physics 2024-09-16 Matthew Kiser , Matthias Beuerle , Fedor Simkovic

We present near-term quantum algorithms for auxiliary-field quantum Monte Carlo (AFQMC), viewed as imaginary-time projection for ground-state calculation as an ensemble of one-body propagators driven by stochastic fields $\Omega$. Starting…

Quantum Physics · Physics 2026-03-31 Xiantao Li

The auxiliary-field quantum Monte Carlo (AFQMC) method provides a computational framework for solving the time-independent Schroedinger equation in atoms, molecules, solids, and a variety of model systems. AFQMC has recently witnessed…

Computational Physics · Physics 2018-08-14 Mario Motta , Shiwei Zhang

Ab initio auxiliary-field quantum Monte Carlo (AFQMC) is a systematically improvable many-body method, but its application to extended solids has been severely limited by unfavorable computational scaling and memory requirements that…

Strongly Correlated Electrons · Physics 2026-02-25 Jinghong Zhang , Meng-Fu Chen , Adam Rettig , Tong Jiang , Paul J. Robinson , Hieu Q. Dinh , Anton Z. Ni , Joonho Lee

We outline how auxiliary-field quantum Monte Carlo (AFQMC) can leverage graphical processing units (GPUs) to accelerate the simulation of solid state sytems. By exploiting conservation of crystal momentum in the one- and two-electron…

Computational Physics · Physics 2020-08-21 Fionn D. Malone , Shuai Zhang , Miguel A. Morales

Quantum Monte Carlo (QMC) techniques are widely used in a variety of scientific problems and much work has been dedicated to developing optimized algorithms that can accelerate QMC on standard processors (CPU). With the advent of various…

Quantum Physics · Physics 2023-04-28 Shuvro Chowdhury , Kerem Y. Camsari , Supriyo Datta

We present an implementation of phaseless Auxiliary-Field Quantum Monte Carlo (ph-AFQMC) utilizing graphical processing units (GPUs). The AFQMC method is recast in terms of matrix operations which are spread across thousands of processing…

Computational Physics · Physics 2018-09-07 James Shee , Evan J. Arthur , Shiwei Zhang , David R. Reichman , Richard A. Friesner

The accurate computation of forces and other energy derivatives has been a long-standing challenge for quantum Monte Carlo methods. A number of technical obstacles contribute to this challenge. We discuss how these obstacles can be removed…

Materials Science · Physics 2023-05-29 Siyuan Chen , Shiwei Zhang

Quantum Monte Carlo (QMC) methods deliver highly accurate electronic structure calculations but are computationally intensive. The quantum Monte Carlo kernel library (QMCkl) provides a modular, portable collection of high-performance…

Computing accurate yet efficient approximations to the solutions of the electronic Schr\"odinger equation has been a paramount challenge of computational chemistry for decades. Quantum Monte Carlo methods are a promising avenue of…

Chemical Physics · Physics 2023-09-25 Zeno Schätzle , Bernát Szabó , Matĕj Mezera , Jan Hermann , Frank Noé

We present a generalization of the phaseless auxiliary-field quantum Monte Carlo (AFQMC) method to cavity quantum-electrodynamical (QED) matter systems. The method can be formulated in both the Coulomb and the dipole gauge. We verify its…

Chemical Physics · Physics 2025-03-04 Lukas Weber , Leonardo dos Anjos Cunha , Miguel A. Morales , Angel Rubio , Shiwei Zhang

We present a framework of an auxiliary field quantum Monte Carlo (QMC) method for multi-orbital Hubbard models. Our formulation can be applied to a Hamiltonian which includes terms for on-site Coulomb interaction for both intra- and…

Strongly Correlated Electrons · Physics 2009-10-30 Yukitoshi Motome , Masatoshi Imada

We extend correlated sampling from classical auxiliary-field quantum Monte Carlo to the quantum-classical (QC-AFQMC) framework, enabling accurate nuclear force computations crucial for geometry optimization and reaction dynamics. Stochastic…

In this study, we propose quantum annealing-enhanced Markov Chain Monte Carlo (QAEMCMC), where QA is integrated into the MCMC subroutine. QA efficiently explores low-energy configurations and overcomes local minima, enabling the generation…

Quantum Physics · Physics 2025-02-13 Shunta Arai , Tadashi Kadowaki

We develop a local correlation variant of auxiliary field quantum Monte Carlo (AFQMC) that is based on local natural orbitals (LNO-AFQMC). In LNO-AFQMC, independent AFQMC calculations are performed for each localized occupied orbital using…

Chemical Physics · Physics 2023-08-25 Jo S. Kurian , Hong-Zhou Ye , Ankit Mahajan , Timothy C. Berkelbach , Sandeep Sharma

Quantum Monte Carlo (QMC) methods are some of the most accurate methods for simulating correlated electronic systems. We investigate the compatibility, strengths and weaknesses of two such methods, namely, diffusion Monte Carlo (DMC) and…

Computational Physics · Physics 2020-10-14 Fionn D. Malone , Anouar Benali , Miguel A. Morales , Michel Caffarel , P. R. C. Kent , Luke Shulenburger

We introduce a new high-performance design for parallelism within the Quantum Monte Carlo code QMCPACK. We demonstrate that the new design is better able to exploit the hierarchical parallelism of heterogeneous architectures compared to the…

Computational Physics · Physics 2023-04-19 Ye Luo , Peter Doak , Paul Kent
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