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We introduce a Metropolis-Hastings Markov chain for Boltzmann distributions of classical spin systems. It relies on approximate tensor network contractions to propose correlated collective updates at each step of the evolution. We present…

We present a high-performance budgeted multi-level Monte Carlo method for estimates on the entire spatial domain of multi-PDE problems with random input data. The method is designed to operate optimally within memory and CPU-time…

Numerical Analysis · Mathematics 2025-06-10 Niklas Baumgarten , Robert Kutri , Robert Scheichl

We provide an extension to lattice systems of the reptation quantum Monte Carlo algorithm, originally devised for continuous Hamiltonians. For systems affected by the sign problem, a method to systematically improve upon the so-called…

Other Condensed Matter · Physics 2010-10-26 Giuseppe Carleo , Federico Becca , Saverio Moroni , Stefano Baroni

Gain-dissipative platforms consisting of lasers, optical parametric oscillators and nonequilibrium condensates operating at the condensation/coherence threshold have been recently proposed as efficient analog simulators of 2-local spin…

Disordered Systems and Neural Networks · Physics 2021-02-10 Nikita Stroev , Natalia G. Berloff

We propose a multi-index algorithm for the Monte Carlo (MC) discretization of a linear, elliptic PDE with affine-parametric input. We prove an error vs. work analysis which allows a multi-level finite-element approximation in the physical…

Numerical Analysis · Mathematics 2019-07-18 Josef Dick , Michael Feischl , Christoph Schwab

An efficient Quantum Monte Carlo algorithm for the simulation of bosonic systems on a lattice in a grand canonical ensemble is proposed. It is based on the mapping of bosonic models to the spin models in the limit of the infinite total spin…

Statistical Mechanics · Physics 2007-05-23 Jurij Smakov , Kenji Harada , Naoki Kawashima

We extend the Longstaff-Schwartz algorithm for approximately solving optimal stopping problems on high-dimensional state spaces. We reformulate the optimal stopping problem for Markov processes in discrete time as a generalized statistical…

Probability · Mathematics 2007-05-23 Daniel Egloff

We describe collective-move Monte Carlo algorithms designed to approximate the overdamped dynamics of self-assembling nanoscale components equipped with strong, short-ranged and anisotropic interactions. Conventional Monte Carlo simulations…

Statistical Mechanics · Physics 2012-04-16 Stephen Whitelam

We propose a new algorithm which works effectively in global updates in Monte Carlo study. We apply it to the quantum spin chain with next-nearest-neighbor interactions. We observe that Monte Carlo results are in excellent agreement with…

Condensed Matter · Physics 2017-02-01 Tomo Munehisa , Yasuko Munehisa

We present a general method for obtaining the exact static solutions and collective excitation frequencies of a trapped Bose-Einstein condensate (BEC) with dipolar atomic interactions in the Thomas-Fermi regime. The method incorporates…

We present an exact quantum Monte Carlo method for spin systems coupled to dissipative bosonic baths which makes use of nonlocal wormhole updates to simulate the retarded spin-flip interactions originating from an off-diagonal spin-boson…

Strongly Correlated Electrons · Physics 2022-05-17 Manuel Weber

In many problems, complex non-Gaussian and/or nonlinear models are required to accurately describe a physical system of interest. In such cases, Monte Carlo algorithms are remarkably flexible and extremely powerful approaches to solve such…

Computation · Statistics 2015-04-23 Thi Le Thu Nguyen , Francois Septier , Gareth W. Peters , Yves Delignon

We present a new efficient method for Monte Carlo simulations of diffusion-reaction processes. First introduced by us in [Phys. Rev. Lett., 97:230602, 2006], the new algorithm skips the traditional small diffusion hops and propagates the…

Materials Science · Physics 2013-05-29 T. Oppelstrup , V. V. Bulatov , A. Donev , M. H. Kalos , G. H. Gilmer , B. Sadigh

We describe a novel simulation method that eliminates the slowing-down problem in the Monte Carlo simulations of imaginary-time path integrals near the continuum limit. This method combines a stochastic blocking procedure with the multigrid…

Statistical Mechanics · Physics 2007-05-23 C. H. Mak , Sergei Zakharov

Sequential Monte Carlo is a family of algorithms for sampling from a sequence of distributions. Some of these algorithms, such as particle filters, are widely used in the physics and signal processing researches. More recent developments…

Computation · Statistics 2013-06-25 Yan Zhou

Population annealing Monte Carlo is an efficient sequential algorithm for simulating k-local Boolean Hamiltonians. Because of its structure, the algorithm is inherently parallel and therefore well suited for large-scale simulations of…

Disordered Systems and Neural Networks · Physics 2018-11-26 Amin Barzegar , Christopher Pattison , Wenlong Wang , Helmut G. Katzgraber

An extension to the multiple-histogram method (sometimes referred to as the Ferrenberg-Swendsen method) for use in quantum Monte Carlo simulations is presented. This method is shown to work well for the 2D repulsive Hubbard model, allowing…

Strongly Correlated Electrons · Physics 2009-10-31 Christopher L. Martin

An analytic operator solution of a generalized quantum mechanical Hamiltonian of two-mode Bose Einstein condensates (BECs) is obtained and the same is used to investigate the nonclassical properties of the modes present in the system.…

Quantum Physics · Physics 2022-06-10 Sandip Kumar Giri , Biswajit Sen , C H Raymond Ooi , Anirban Pathak

Polaron tunneling is a prominent example of a problem characterized by different energy scales, for which the standard quantum Monte Carlo methods face a slowdown problem. We propose a new quantum-tunneling Monte Carlo (QTMC) method which…

Quantum Gases · Physics 2021-04-14 A. S. Popova , V. V. Tiunova , A. N. Rubtsov

Process monitoring and control requires detection of structural changes in a data stream in real time. This article introduces an efficient sequential Monte Carlo algorithm designed for learning unknown changepoints in continuous time. The…

Applications · Statistics 2015-09-29 Melissa J. M. Turcotte , Nicholas A. Heard
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