Quantum speedup of classical mixing processes
arXiv:quant-ph/0609204 · doi:10.1103/PhysRevA.76.042306
Abstract
Most approximation algorithms for #P-complete problems (e.g., evaluating the permanent of a matrix or the volume of a polytope) work by reduction to the problem of approximate sampling from a distribution over a large set . This problem is solved using the {\em Markov chain Monte Carlo} method: a sparse, reversible Markov chain on with stationary distribution is run to near equilibrium. The running time of this random walk algorithm, the so-called {\em mixing time} of , is as shown by Aldous, where is the spectral gap of and is the minimum value of . A natural question is whether a speedup of this classical method to , the diameter of the graph underlying , is possible using {\em quantum walks}. We provide evidence for this possibility using quantum walks that {\em decohere} under repeated randomized measurements. We show: (a) decoherent quantum walks always mix, just like their classical counterparts, (b) the mixing time is a robust quantity, essentially invariant under any smooth form of decoherence, and (c) the mixing time of the decoherent quantum walk on a periodic lattice is , which is indeed and is asymptotically no worse than the diameter of (the obvious lower bound) up to at most a logarithmic factor.
13 pages; v2 revised several parts
References in corpus (5)
Cited by in corpus (45)
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