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Permutation matrix representation quantum Monte Carlo

Lalit Gupta, Tameem Albash, Itay Hen

2020Journal of Statistical Mechanics Theory and Experiment14 citationsDOIOpen Access PDF

Abstract

Abstract 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. In our approach, the quantum dimension consists of products of elements of a permutation group. As such, it allows for the study of a very wide variety of models on an equal footing. To demonstrate the utility of our technique, we use it to clarify the emergence of the sign problem in the simulations of non-stoquastic physical models. We showcase the flexibility of our algorithm and the advantages it offers over existing state-of-the-art by simulating transverse-field Ising model Hamiltonians and comparing the performance of our technique against that of the stochastic series expansion algorithm. We also study a transverse-field Ising model augmented with randomly chosen two-body transverse-field interactions.

Topics & Concepts

Ising modelStatistical physicsQuantum annealingQuantum Monte CarloQuantumSeries (stratigraphy)Monte Carlo methodMathematicsPartition function (quantum field theory)Series expansionQuantum computerQuantum algorithmRepresentation (politics)Computer scienceApplied mathematicsDimension (graph theory)Permutation (music)AlgorithmMonte Carlo algorithmPower seriesHybrid Monte CarloMonte Carlo method in statistical physicsEffective dimensionMonte Carlo molecular modelingQuantum Computing Algorithms and ArchitectureQuantum many-body systemsMarkov Chains and Monte Carlo Methods
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