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We introduce an efficient approach to implement correlated many-body trial wave functions in auxiliary-field quantum Monte Carlo (AFQMC). To control the sign/phase problem in AFQMC, a constraint is derived from an exact gauge condition but…

强关联电子 · 物理学 2025-10-27 Zhi-Yu Xiao , Zixiang Lu , Yixiao Chen , Tao Xiang , Shiwei Zhang

We explore different ways of incorporating accurate trial wave functions into free projection auxiliary field quantum Monte Carlo (fp-AFQMC). Trial states employed include coupled cluster singles and doubles, multi-Slater, and symmetry…

化学物理 · 物理学 2021-04-15 Ankit Mahajan , Sandeep Sharma

We describe and discuss a recently proposed quantum Monte Carlo algorithm to compute the ground-state properties of various systems of interacting fermions. In this method, the ground state is projected from an initial wave function by a…

凝聚态物理 · 物理学 2009-10-28 Shiwei Zhang , J. Carlson , J. E. Gubernatis

We propose a new quantum Monte Carlo algorithm to compute fermion ground-state properties. The ground state is projected from an initial wavefunction by a branching random walk in an over-complete basis space of Slater determinants. By…

凝聚态物理 · 物理学 2016-08-31 Shiwei Zhang , J. Carlson , J. E. Gubernatis

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…

化学物理 · 物理学 2025-01-13 Tong Jiang , Bryan O'Gorman , Ankit Mahajan , Joonho Lee

We present a formulation of the Constrained Path Monte Carlo (CPMC) method for fermions that uses trial wave-functions that include many-body effects. This new formulation allows us to implement a whole family of generalized mean-field…

超导电性 · 物理学 2009-10-31 M. Guerrero , G. Ortiz , J. E. Gubernatis

We present a method to construct pseudo-BCS wave functions from the one-body density matrix. The resulting many-body wave function, which can be produced for any fermion systems, including those with purely repulsive interactions, has the…

强关联电子 · 物理学 2021-01-27 Zhiyu Xiao , Hao Shi , Shiwei Zhang

We marry tensor network states (TNS) and projector quantum Monte Carlo (PMC) to overcome the high computational scaling of TNS and the sign problem of PMC. Using TNS as trial wavefunctions provides a route to systematically improve the sign…

强关联电子 · 物理学 2014-07-04 Sebastian Wouters , Brecht Verstichel , Dimitri Van Neck , Garnet Kin-Lic Chan

We propose a new scheme to implement the self-consistent optimization of the trial wave-function in constrained path auxiliary field Quantum Monte Carlo (CP-AFQMC) in the framewok of natural orbitals. In this scheme, a new trial…

强关联电子 · 物理学 2023-08-15 Mingpu Qin

Ultracold-atom simulations of the Hubbard model provide insights into the character of charge and spin correlations in and out of equilibrium. The corresponding numerical simulations, on the other hand, remain a significant challenge. We…

强关联电子 · 物理学 2023-01-25 Veljko Janković , Jakša Vučičević

For optimal accuracy, auxiliary-field quantum Monte Carlo (AFQMC) requires trial states consisting of multiple Slater determinants. We develop an efficient algorithm to select the determinants from an AFQMC random walk eliminating the need…

化学物理 · 物理学 2025-07-08 Zoran Sukurma , Martin Schlipf , Georg Kresse

The Auxiliary-Field Quantum Monte Carlo (AFQMC) algorithm is a powerful quantum many-body method that can be used successfully as an alternative to standard quantum chemistry approaches to compute the ground state of many body systems, such…

化学物理 · 物理学 2018-07-23 Edgar Josué Landinez Borda , John A. Gomez , Miguel A. Morales

We propose a novel algorithm for calculating the ground-state energy of quantum many-body systems by combining auxiliary-field quantum Monte Carlo (AFQMC) with tensor-train sketching. In AFQMC, a good trial wavefunction to guide the random…

数值分析 · 数学 2026-02-17 Ziang Yu , Shiwei Zhang , Yuehaw Khoo

We introduce a method that combines the power of both the lattice Green function Monte Carlo (LGFMC) with the auxiliary field techniques (AFQMC), and allows us to compute exact ground state properties of the Hubbard model for U<~ 4t on…

强关联电子 · 物理学 2015-06-03 Sandro Sorella

Optical lattice experiments with ultracold fermion atoms and quantum gas microscopy have recently realized direct measurements of magnetic correlations at the site-resolved level. We calculate the short-range spin correlation functions in…

强关联电子 · 物理学 2017-08-28 Mingpu Qin , Hao Shi , Shiwei Zhang

The so-called phaseless quantum Monte-Carlo method currently offers one of the best performing theoretical framework to investigate interacting Fermi systems. It allows to extract an approximate ground-state wavefunction by averaging…

强关联电子 · 物理学 2017-03-31 Olivier Juillet , Alexandre Leprévost , Jérémy Bonnard , Raymond Frésard

Quantum Monte-Carlo (QMC) simulations involving fermions have the notorious sign problem. Some well-known exceptions of the auxiliary field QMC algorithm rely on the factorizibility of the fermion determinant. Recently, a fermionic QMC…

强关联电子 · 物理学 2009-02-06 Congjun Wu , Shou-Cheng Zhang

We propose a general framework for finding the ground state of many-body fermionic systems by using feed-forward neural networks. The anticommutation relation for fermions is usually implemented to a variational wave function by the Slater…

强关联电子 · 物理学 2021-12-21 Koji Inui , Yasuyuki Kato , Yukitoshi Motome

We introduce an efficient approach to implement neural network quantum states (NNQS) as trial wavefunctions in auxiliary-field quantum Monte Carlo (AFQMC). NNQS are a recently developed class of variational ans\"atze capable of flexibly…

化学物理 · 物理学 2025-10-07 Zhi-Yu Xiao , Bowen Kan , Huan Ma , Bowen Zhao , Honghui Shang

To make useful connections with experimental measurements, correlated electronic structure theories must accurately predict chemical properties in addition to energies. We present a finite-difference based algorithm to compute first-order…

化学物理 · 物理学 2025-09-24 Leon Otis , Sri Gudivada , Marvin Friede , James Shee
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