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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.

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}).