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相关论文: Time-dependent quantum Monte Carlo and the stochas…

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We present a statistical framework for extracting spatially resolved entanglement directly from an ensemble of marginal (one-body) wavefunctions in Time-Dependent Quantum Monte Carlo (TDQMC). Treating the guide waves as a statistical…

量子物理 · 物理学 2026-05-05 Ivan P. Christov

We present a new approach to the study of equilibrium properties in many-body quantum physics. Our method takes inspiration from Density Matrix Quantum Monte Carlo and incorporates new crucial features. First of all, the dynamics is…

量子物理 · 物理学 2022-01-06 Romain Chessex , Massimo Borrelli , Hans Christian Öttinger

High-order perturbative $\textit{ab initio}$ calculations are challenging due to the rapidly growing configuration space and the difficulty of assessing convergence. In this letter, we introduce perturbation theory quantum Monte Carlo…

核理论 · 物理学 2026-05-06 Xin Zhen , Rongzhe Hu , Junchen Pei , Furong Xu

Diffusion Monte Carlo (DMC) based on fixed-node approximation has enjoyed significant developments in the past decades and become one of the go-to methods when accurate ground state energy of molecules and materials is needed. The remaining…

化学物理 · 物理学 2023-08-07 Weiluo Ren , Weizhong Fu , Xiaojie Wu , Ji Chen

We present a method based on the Path Integral Monte Carlo formalism for the calculation of ground-state time correlation functions in quantum systems. The key point of the method is the consideration of time as a complex variable whose…

统计力学 · 物理学 2015-06-24 Riccardo Rota , Joaquim Casulleras , Ferran Mazzanti , Jordi Boronat

We present a unified theory of the variational Monte Carlo (VMC) and determinant quantum Monte Carlo (DQMC) methods using a novel density matrix formulation of VMC. We introduce an efficient algorithm for VMC to compute correlation…

强关联电子 · 物理学 2018-10-02 Mohammad-Sadegh Vaezi , Abolhassan Vaezi

The Dynamic Monte Carlo (DMC) method is an established molecular simulation technique for the analysis of the dynamics in colloidal suspensions. An excellent alternative to Brownian Dynamics or Molecular Dynamics simulation, DMC is…

软凝聚态物质 · 物理学 2020-07-15 Fabián A. García Daza , Alejandro Cuetos , Alessandro Patti

Quantum-mechanical methods are widely used for understanding molecular interactions throughout biology, chemistry, and materials science. Quantum diffusion Monte Carlo (DMC) and coupled cluster with single, double, and perturbative triple…

The variational quantum Monte Carlo (VQMC) method received significant attention in the recent past because of its ability to overcome the curse of dimensionality inherent in many-body quantum systems. Close parallels exist between VQMC and…

分布式、并行与集群计算 · 计算机科学 2021-07-01 Tianchen Zhao , Saibal De , Brian Chen , James Stokes , Shravan Veerapaneni

We present a method which extends Monte Carlo studies to situations that require a large dynamic range in particle number. The underlying idea is that, in order to calculate the collisional evolution of a system, some particle interactions…

天体物理学 · 物理学 2009-11-13 C. W. Ormel , M. Spaans

Quantum dynamical time-evolution of bosonic fields is shown to be equivalent to a stochastic trajectory in space-time, corresponding to samples of a statistical mechanical steady-state in a higher dimensional quasi-time. This is proved…

量子物理 · 物理学 2021-03-24 Peter D. Drummond

We propose a new time quantifiable Monte Carlo (MC) method to simulate the thermally induced magnetization reversal for an isolated single domain particle system. The MC method involves the determination of density of states, and the use of…

统计力学 · 物理学 2009-11-11 X. Z. Cheng , M. B. A. Jalil , H. K. Lee , Y. Okabe

Direct sampling of multi-dimensional systems with quantum Monte Carlo methods allows exact account of many-body effects or particle correlations. The most straightforward approach to solve the Schr\"odinger equation, Diffusion Monte Carlo,…

量子物理 · 物理学 2017-09-07 Ilkka Ruokosenmäki , Tapio T. Rantala

The discrete time path integral Monte Carlo (PIMC) with a one-particle density matrix approximation is applied to study the quantum phase transition in the coupled double-well chain. To improve the convergence properties, the exact action…

统计力学 · 物理学 2015-06-25 Dong-Hee Kim , Yu-Cheng Lin , Heiko Rieger

Quantum computers have a potential for solving quantum chemistry problems with higher accuracy than classical computers. Quantum computing quantum Monte Carlo (QC-QMC) is a QMC with a trial state prepared in quantum circuit, which is…

量子物理 · 物理学 2024-06-07 Shu Kanno , Hajime Nakamura , Takao Kobayashi , Shigeki Gocho , Miho Hatanaka , Naoki Yamamoto , Qi Gao

The nonequilibrium dynamics of correlated charge transfer along a one-dimensional chain in presence of a phonon environment is investigated within a dissipative Hubbard model. For this generalization of the ubiquitous spin-boson model the…

统计力学 · 物理学 2009-11-11 Lothar Muehlbacher , Joachim Ankerhold

Because of their robustness, efficiency and non-intrusiveness, Monte Carlo methods are probably the most popular approach in uncertainty quantification to computing expected values of quantities of interest (QoIs). Multilevel Monte Carlo…

数值分析 · 数学 2022-04-12 Marcus J. Grote , Simon Michel , Fabio Nobile

Quantum Monte Carlo methods are first-principle approaches that approximately solve the Schr\"odinger equation stochastically. As compared to traditional quantum chemistry methods, they offer important advantages such as the ability to…

化学物理 · 物理学 2020-02-11 Jonas Feldt , Claudia Filippi

We formulate a quantum Monte Carlo (QMC) method for calculating the ground state of many-boson systems. The method is based on a field-theoretical approach, and is closely related to existing fermion auxiliary-field QMC methods which are…

计算物理 · 物理学 2009-11-10 Wirawan Purwanto , Shiwei Zhang

We propose a method for simulating the stochastic dynamics of classical spin systems with long-range interactions. The method incorporates the stochastic cutoff (SCO) method, which is originally specialized for simulating equilibrium state,…

统计力学 · 物理学 2019-12-09 Taichi Hinokihara , Yuta Okuyama , Munetaka Sasaki , Seiji Miyashita