Quantum Monte Carlo: What can random numbers tell us about correlated quantum systems?

Marjan Maček, Philipp T. Dumitrescu, Corentin Bertrand +3 more

S. Rombouts, K. Heyde

Abstract Over the past decades, quantum Monte Carlo methods have proven to be very valuable for the study of quantum‐mechanical many‐body systems. The basic ingredient of theses methods is a decomposition of the imaginary‐time evolution operator into path integrals. These paths can then be sampled using Monte Carlo techniques. Though very successful for bosons, these methods often have troubles in dealing with fermions: the fermionic antisymmetry leads to a ‘sign problem’. One approach, the auxiliary‐field method, has yielded slightly better results in dealing with the sign problem. Some recent developments on this method are highlighted. As a case study, it is applied to a pairing model for Fe nuclei, where indications are found for a phase transition related to nucleon–nucleon pairing.

Yutaka Okabe, Ishizuka Jun-ichi, 岡部 豊 +1 more

Matthew Clifford Wilson

Masatoshi Imada, Minoru Takahashi

A quantum transfer matrix method is proposed and examined. To obtain finite temperature properties, a small number of Monte Carlo samples for the trace summation is taken without the Monte Carlo sampling of the path integral. We introduce the method of a random orthonormal base in the Monte Carlo sampling. This makes it possible to investigate larger size systems than the exact diagonalization. An advantage of this method is that it does not have negative sign difficulty in the path integral as contrast to the usual quantum Monte Carlo method. A quantum molecular dynamics method is also proposed to investigate dynamical correlation functions.

John M. Martyn, Joshua Lin, Neill C. Warrington +2 more

Quantum Monte Carlo (QMC) algorithms are among the most powerful classical methods for simulating quantum systems, yet their accuracy is often limited by the systematic errors in the approximations used, such as Trotterization. Here we introduce randomly compiled quantum Monte Carlo (RC-QMC) as a general framework that suppresses these systematic errors by averaging over a family of approximations rather than relying on a single fixed one. This strategy is grounded in the concept of randomized compiling from quantum computing, which suppresses errors by sampling over quantum gates, at essentially no additional computational cost. Consequently, our framework achieves a computational advantage over standard QMC methods when estimating a target state to a desired level of accuracy. We illustrate this advantage on two key Monte Carlo algorithms: (1) path integral quantum Monte Carlo for estimating thermal states, and (2) the quantum trajectories method for simulating open system dynamics. In aggregate, these results represent a cross-fertilization of quantum and classical algorithms, are readily generalizable to other QMC methods, and suggest wider applications in classical simulation.