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Monte Carlo algorithms are barely considered in spin foam quantum gravity. Due to the quantum nature of spin foam amplitudes one cannot readily apply them, and the present sign problem is a threat to convergence and thus efficiency. Yet,…

General Relativity and Quantum Cosmology · Physics 2024-07-25 Sebastian Steinhaus

Hamiltonian Monte Carlo is a widely used algorithm for sampling from posterior distributions of complex Bayesian models. It can efficiently explore high-dimensional parameter spaces guided by simulated Hamiltonian flows. However, the…

Computation · Statistics 2019-04-29 Lingge Li , Andrew Holbrook , Babak Shahbaba , Pierre Baldi

Quantum Monte Carlo (QMC) methods are the gold standard for studying equilibrium properties of quantum many-body systems -- their phase transitions, ground and thermal state properties. However, in many interesting situations QMC methods…

Quantum Physics · Physics 2020-08-19 Dominik Hangleiter , Ingo Roth , Daniel Nagaj , Jens Eisert

Quantum Monte Carlo simulations provide one of the more powerful and versatile numerical approaches to condensed matter systems. However, their application to frustrated quantum spin models, in all relevant temperature regimes, is hamstrung…

Strongly Correlated Electrons · Physics 2017-07-20 Stefan Wessel , B. Normand , Frédéric Mila , Andreas Honecker

We introduce a Quantum Monte Carlo (QMC) method which efficiently simulates in a sign-problem-free way a broad class of frustrated $S=1/2$ models with competing antiferromagnetic interactions. Our scheme uses the basis of total spin…

Strongly Correlated Electrons · Physics 2016-11-09 Fabien Alet , Kedar Damle , Sumiran Pujari

The Worldvolume Hybrid Monte Carlo (WV-HMC) method [arXiv:2012.08468] is an efficient algorithm for addressing the numerical sign problem at moderate computational cost. It mitigates the sign problem while avoiding the ergodicity issues…

Strongly Correlated Electrons · Physics 2026-05-15 Masafumi Fukuma , Yusuke Namekawa

In this talk we show how the sign problem, occurring in dynamical simulations of random matrices at nonzero chemical potential, can be avoided by judiciously combining matrices into subsets. One can prove that these subsets have real and…

High Energy Physics - Lattice · Physics 2011-11-22 Jacques C. R. Bloch

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…

Strongly Correlated Electrons · Physics 2009-02-06 Congjun Wu , Shou-Cheng Zhang

We present a novel strategy to strongly reduce the severity of the sign problem, using line integrals along paths of changing imaginary action. Highly oscillating regions along these paths cancel out, decreasing their contributions. As a…

High Energy Physics - Lattice · Physics 2023-10-18 Rasmus N. Larsen

The quantum Monte Carlo algorithm is arguably one of the most powerful computational many-body methods, enabling accurate calculation of many properties in interacting quantum systems. In the presence of the so-called sign problem, the…

Strongly Correlated Electrons · Physics 2018-02-23 Chia-Chen Chang , Miguel A. Morales

We propose a projection approach to perform quantum Monte Carlo (QMC) simulation on the infinite-$U$ Hubbard model at some integer fillings where either it is sign problem free or surprisingly has an algebraic sign structure -- a power law…

Strongly Correlated Electrons · Physics 2021-12-28 Yunqing Ouyang , Xiao Yan Xu

The simulation of quantum many-body systems poses a significant challenge in physics due to the exponential scaling of Hilbert space with the number of particles. Traditional methods often struggle with large system sizes and frustrated…

Materials Science · Physics 2024-05-27 Avishek Singh , Nirmal Ganguli

We discuss the Fermion sign problem and, by examining a very general Hubbard-Stratonovich (HS) transformation, argue that the sign problem cannot be solved with such methods. We propose a different kind of transformation which, while not…

Condensed Matter · Physics 2011-08-11 Ghassan George Batrouni , Philippe de Forcrand

Markov chain Monte Carlo (MCMC) is a powerful tool for sampling from complex probability distributions. Despite its versatility, MCMC often suffers from strong autocorrelation and the negative sign problem, leading to slowing down the…

Statistical Mechanics · Physics 2024-12-05 Synge Todo

Insurance companies make extensive use of Monte Carlo simulations in their capital and solvency models. To overcome the computational problems associated with Monte Carlo simulations, most large life insurance companies use proxy models…

Computational Finance · Quantitative Finance 2023-06-22 Lucio Fernandez-Arjona

Monte Carlo studies of many quantum systems face exponentially severe signal-to-noise problems. We show that noise arising from complex phase fluctuations of observables can be reduced without introducing bias using path integral contour…

High Energy Physics - Lattice · Physics 2020-08-05 William Detmold , Gurtej Kanwar , Michael L. Wagman , Neill C. Warrington

This work shows that the recently discovered operator contraction identity for solving the discreet Path Integral of the harmonic oscillator can be applied equally to fermions in any dimension. This then yields an exactly solvable model for…

Strongly Correlated Electrons · Physics 2026-04-21 Siu A. Chin

The multilevel blocking algorithm recently proposed as a possible solution to the sign problem in path-integral Monte Carlo simulations has been extended to systems with long-ranged interactions along the Trotter direction. As an…

Chemical Physics · Physics 2009-11-06 R. Egger , L. Muehlbacher , C. H. Mak

For important classes of many-fermion problems, quantum Monte Carlo (QMC) methods allow exact calculations of ground-state and finite-temperature properties, without the sign problem. The list spans condensed matter, nuclear physics, and…

Computational Physics · Physics 2016-03-23 Hao Shi , Shiwei Zhang

Despite their exceptional flexibility and popularity, the Monte Carlo methods often suffer from slow mixing times for challenging statistical physics problems. We present a general strategy to overcome this difficulty by adopting ideas and…

Computational Physics · Physics 2017-01-06 Li Huang , Lei Wang