Results 71 to 80 of about 637,276 (316)

Unifying static and dynamic properties in 3D quantum antiferromagnets

, 2017
Quantum Monte Carlo simulations offer an unbiased means to study the static and dynamic properties of quantum critical systems, while quantum field theory provides direct analytical results.
Kharkov, Y., Meng, Zi Yang, Normand, B., Qin, Yan Qi, Scammell, H. D., Sushkov, O. P.   +5 more
core   +1 more source

Permutation matrix representation quantum Monte Carlo [PDF]

Journal of Statistical Mechanics: Theory and Experiment, 2020
We present a quantum Monte Carlo algorithm for the simulation of general quantum and classical many-body models within a single unifying framework. The algorithm builds on a power series expansion of the quantum partition function in its off-diagonal terms and is both parameter-free and Trotter error-free.
Gupta, Lalit, Albash, Tameem, Hen, Itay
openaire   +2 more sources

Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning

SciPost Physics, 2023
Adiabatic quantum computers, such as the quantum annealers commercialized by D-Wave Systems Inc., are routinely used to tackle combinatorial optimization problems.
Giuseppe Scriva, Emanuele Costa, Benjamin McNaughton, Sebastiano Pilati
doaj   +1 more source

Monte Carlo Hamiltonian - From Statistical Physics to Quantum Theory

, 1999
Monte Carlo techniques have been widely employed in statistical physics as well as in quantum theory in the Lagrangian formulation. However, in some areas of application to quantum theories computational progress has been slow. Here we present a recently
Barnes, Binder, Chin, Chin, Chun-Qing Huang, Creutz, de Saavedra, Hamza Jirari, Helmut Kröger, Honma, Jirari, Jun-Qin Jiang, Kevin J.M. Moriarty, Marziani, von der Linden, Xiang-Qian Luo   +15 more
core   +2 more sources

Breakdown of the Migdal-Eliashberg theory: A determinant quantum Monte Carlo study [PDF]

, 2017
The superconducting (SC) and charge-density-wave (CDW) susceptibilities of the two dimensional Holstein model are computed using determinant quantum Monte Carlo (DQMC), and compared with results computed using the Migdal-Eliashberg (ME) approach.
I. Esterlis, B. Nosarzewski, E. Huang, B. Moritz, T. Devereaux, D. Scalapino, S. Kivelson   +6 more
semanticscholar   +1 more source

Ab Initio Finite Temperature Auxiliary Field Quantum Monte Carlo. [PDF]

Journal of Chemical Theory and Computation, 2018
We present an ab initio auxiliary field quantum Monte Carlo method for studying the electronic structure of molecules, solids, and model Hamiltonians at finite temperature. The algorithm marries the ab initio phaseless auxiliary field quantum Monte Carlo
Yuan Liu, Minsik Cho, B. Rubenstein
semanticscholar   +1 more source

Unique Performance Considerations for Printable Organic Semiconductor and Perovskite Radiation Detectors: Toward Consensus on Best Practice Evaluation

Advanced Functional Materials, EarlyView.
A lack of standard approaches for testing and reporting the performance of metal halide perovskites and organic semiconductor radiation detectors has resulted in inconsistent interpretation of performance parameters, impeding progress in the field. This Perspective recommends key metrics and experimental details, which are suggested for reporting in ...
Jessie A. Posar, Matthew Large, Andrea Ciavatti, Laura Basiricò, Beatrice Fraboni, Paul J. Sellin, Attila J. Mozer, Marco Petasecca, Matthew J. Griffith   +8 more
wiley   +1 more source

A Quantum Monte Carlo Method at Fixed Energy

, 2009
In this paper we explore new ways to study the zero temperature limit of quantum statistical mechanics using Quantum Monte Carlo simulations. We develop a Quantum Monte Carlo method in which one fixes the ground state energy as a parameter.
Beard, David Gosset, Edward Farhi, Farhi, Farhi, Farhi, Harvey B. Meyer, Jeffrey Goldstone, Krzakala, Newman, Prokofʼev, Sandvik, Sandvik, Suzuki, Wolff, Wolff   +15 more
core   +1 more source

Convergence theorems for quantum annealing [PDF]

, 2006
We prove several theorems to give sufficient conditions for convergence of quantum annealing, which is a protocol to solve generic optimization problems by quantum dynamics.
Aarts E, Das A, Farhi E Goldstone J Gutmann S Sipser M, Garey M R, Geman S, Hidetoshi Nishimori, Kadowaki T, Landau D P, Lee Y-H, Nishimori H, Santoro G E, Sarjala M, Satoshi Morita, Stella L Santoro G E, Tanaka K   +14 more
core   +4 more sources

A combined variational and diagrammatic quantum Monte Carlo approach to the many-electron problem

Nature Communications, 2019
Two of the most influential ideas developed by Richard Feynman are the Feynman diagram technique and his variational approach. Here we show that combining both, and introducing a diagrammatic quantum Monte Carlo method, results in a powerful and accurate
Kun Chen, K. Haule
semanticscholar   +1 more source