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A numerical algorithm for studying strongly correlated electron systems is proposed. The groundstate wavefunction is projected out after numerical renormalization procedure in the path integral formalism. The wavefunction is expressed from…

Strongly Correlated Electrons · Physics 2007-05-23 Masatoshi Imada , Tsuyoshi Kashima

We present a simple and efficient approximation scheme which greatly facilitates extension of Wang-Landau sampling (or similar techniques) in large systems for the estimation of critical behavior. The method, presented in an algorithmic…

Statistical Mechanics · Physics 2007-05-23 A. Malakis , A. Peratzakis , N. G. Fytas

We present a suite of "holographic" quantum algorithms for efficient ground-state preparation and dynamical evolution of correlated spin-systems, which require far-fewer qubits than the number of spins being simulated. The algorithms…

We propose a new generalized-ensemble algorithm, which we refer to as the multicanonical-multioverlap algorithm. By utilizing a non-Boltzmann weight factor, this method realizes a random walk in the multi-dimensional, energy-overlap space…

Statistical Mechanics · Physics 2009-11-11 Satoru G. Itoh , Yuko Okamoto

We consider a multiscale strategy addressing the disparate scales in the Landau-Lifschitz equations in micro-magnetism. At the microscopic scale, the dynamics of magnetic moments are driven by a high frequency field. On the macroscopic…

Numerical Analysis · Mathematics 2017-03-16 Doghonay Arjmand , Stefan Engblom , Gunilla Kreiss

Random Walk is a basic algorithm to explore the structure of networks, which can be used in many tasks, such as local community detection and network embedding. Existing random walk methods are based on single networks that contain limited…

Social and Information Networks · Computer Science 2023-07-06 Dongsheng Luo , Yuchen Bian , Yaowei Yan , Xiong Yu , Jun Huan , Xiao Liu , Xiang Zhang

Neural quantum states efficiently represent many-body wavefunctions with neural networks, but the cost of Monte Carlo sampling limits their scaling to large system sizes. Here we address this challenge by combining sparse Boltzmann machine…

As compute power increases with time, more involved and larger simulations become possible. However, it gets increasingly difficult to efficiently use the provided computational resources. Especially in particle-based simulations with a…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-08-05 Sebastian Eibl , Ulrich Rüde

An ensemble of 2d disordered clusters with a few electrons is studied as a function of the Coulomb energy to kinetic energy ratio r_s. Between the Fermi system (small r_s) and the Wigner molecule (large r_s), an interaction induced…

Disordered Systems and Neural Networks · Physics 2015-06-24 Franck Selva , Jean-Louis Pichard

Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct…

Computational Physics · Physics 2016-08-15 Xikai Jiang , Jiyuan Li , Xujun Zhao , Jian Qin , Dmitry Karpeev , Juan Hernandez-Ortiz , Juan de Pablo , Olle Heinonen

A non-ergodic quantum state of a many body system is in general random as well as multi-parametric, former due to a lack of exact information due to complexity and latter reflecting its varied behavior in different parts of the Hilbert…

Quantum Physics · Physics 2023-06-21 Devanshu Shekhar , Pragya Shukla

We use statistical mechanics to study model-based Bayesian data clustering. In this approach, each partition of the data into clusters is regarded as a microscopic system state, the negative data log-likelihood gives the energy of each…

Disordered Systems and Neural Networks · Physics 2019-11-19 Alexander Mozeika , Anthony CC Coolen

This work aims to improve the sample efficiency of parallel large-scale ranking and selection (R&S) problems by leveraging correlation information. We modify the commonly used "divide and conquer" framework in parallel computing by adding a…

Methodology · Statistics 2026-02-16 Zishi Zhang , Yijie Peng

An efficient parallelization approach to simulate optical properties of ensembles of quantum emitters in realistic electromagnetic environments is considered. It relies on balancing computing load of utilized processors and is built into…

Computational Physics · Physics 2023-02-01 Maxim Sukharev

Quantum walks, both discrete and continuous, serve as fundamental tools in quantum information processing with diverse applications. This work introduces a hybrid quantum walk model that integrates the coin mechanism of discrete walks with…

Quantum Physics · Physics 2025-09-12 Tianen Chen , Yun Shang

A memory efficient approach to ensembling neural networks is to share most weights among the ensembled models by means of a single reference network. We refer to this strategy as Embedded Ensembling (EE); its particular examples are…

Machine Learning · Statistics 2022-02-25 Maksim Velikanov , Roman Kail , Ivan Anokhin , Roman Vashurin , Maxim Panov , Alexey Zaytsev , Dmitry Yarotsky

Motivated by the ability of triangular spin ladders to implement quantum information processing, we propose a type of such systems whose Hamiltonian includes the XX Heisenberg interaction on the rungs and DzyaloshinskiiMoriya (DM) coupling…

Quantum Physics · Physics 2020-06-19 Sajedeh Shahsavari , Mostafa Motamedifar , Hassan Safari

All-electron calculations play an important role in density functional theory, in which improving computational efficiency is one of the most needed and challenging tasks. In the model formulations, both nonlinear eigenvalue problem and…

Computational Physics · Physics 2020-07-29 Bin Gao , Guanghui Hu , Yang Kuang , Xin Liu

Random walk centrality is a fundamental metric in graph mining for quantifying node importance and influence, defined as the weighted average of hitting times to a node from all other nodes. Despite its ability to capture rich graph…

Artificial Intelligence · Computer Science 2025-10-24 Changan Liu , Zixuan Xie , Ahad N. Zehmakan , Zhongzhi Zhang

We construct a zig-zag process targeting a posterior distribution defined on a hybrid state space consisting of both discrete and continuous variables. The construction does not require any assumptions on the structure among discrete…

Computation · Statistics 2024-01-30 Jere Koskela