A Quantum Parallel Markov Chain Monte Carlo

The UCI Department of Statistics is proud to present Andrew Holbrook, Assistant Professor, Department of Biostatistics, UCLA. The UCI community is invited to join us in DBH 6011.

Abstract:
I propose a novel quantum computing strategy for parallel MCMC algorithms that generate multiple proposals at each step. This strategy makes parallel MCMC amenable to quantum parallelization by using the Gumbel-max trick to turn the generalized accept-reject step into a discrete optimization problem. This allows me to embed target density evaluations within a well-known extension of Grover’s quantum search algorithm. Letting P denote the number of proposals in a single MCMC iteration, the combined strategy reduces the number of target evaluations required from O(P) to O(P^{1/2}).