Markov chain Monte Carlo (MCMC) is the widely-used classical method of random sampling from a probability distribution π by simulating a Markov chain which "mixes" to π at equilibrium. Despite the success quantum walks have been shown to have in speeding up random walk algorithms for search problems ("hitting") and simulated annealing, it remains to prove a general speedup theorem for MCMC sampling algorithms. We review the progress toward this end, in particular using decoherent quantum walks. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Richter, P. C. (2008). The quantum complexity of Markov chain Monte Carlo. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5028 LNCS, pp. 511–522). Springer Verlag. https://doi.org/10.1007/978-3-540-69407-6_55
Mendeley helps you to discover research relevant for your work.