2020/10/23 by Acc Coolen, ACC Coolen, Coolen, ACC +2
Computer Science · Mathematics · Physics and Astronomy · #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph) #Quantum many-body systems #Statistical Mechanics (cond-mat.stat-mech) #cond-mat.stat-mech #quant-ph #stat.CO
paper · pdf · doi:10.48550/arxiv.2010.12334
21 pages, 1 figure
arxiv created 2020/10/23 · openalex publication_date 2020/10/23 · arxiv updated 2020/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Quantum annealing aims to provide a faster method for finding the minima of complicated functions, compared to classical computing, so there is an increasing interest in the relaxation dynamics of quantum spin systems. Moreover, it is known that problems in quantum annealing caused by first order phase transitions can be reduced via appropriate temporal adjustment of control parameters, aimed at steering the system away from local minima. To do this optimally, it would be helpful to predict the evolution of the system at the level of macroscopic observables. Solving the dynamics of a quantum ensemble is nontrivial, as it requires modelling not just the quantum spin system itself but also its interaction with the environment, with which it exchanges energy. An interesting alternative approach to the dynamics of quantum spin systems was proposed about a decade ago. It involves creating a stochastic proxy dynamics via the Suzuki-Trotter mapping of the quantum ensemble to a classical one (the quantum Monte Carlo method), and deriving from this new dynamics closed macroscopic equations for macroscopic observables, using the dynamical replica method. In this chapter we give an introduction to this approach, focusing on the ideas and assumptions behind the derivations, and on its potential and limitations.