A quantum parallel Markov chain Monte Carlo

• URL: http://arxiv.org/abs/2112.00212v4
• Date: Sun, 19 Mar 2023 22:06:58 GMT
• Title: A quantum parallel Markov chain Monte Carlo
• Authors: Andrew J. Holbrook
• Abstract summary: We use the Gumbel-max trick to turn the generalized accept-reject step into a discrete optimization problem. We embed target density evaluations within a well-known extension of Grover's quantum search algorithm.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel hybrid quantum computing strategy for parallel MCMC algorithms that generate multiple proposals at each step. This strategy makes the rate-limiting step within parallel MCMC amenable to quantum parallelization by using the Gumbel-max trick to turn the generalized accept-reject step into a discrete optimization problem. When combined with new insights from the parallel MCMC literature, such an approach allows us 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 $\mathcal{O}(P)$ to $\mathcal{O}(P^{1/2})$. In the following, we review the rudiments of quantum computing, quantum search and the Gumbel-max trick in order to elucidate their combination for as wide a readership as possible.

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