Modernizing quantum annealing using local searches

Chancellor, N.

Abstract

I describe how real quantum annealers may be used to perform local (in state space) searches around specified states, rather than the global searches traditionally implemented in the quantum annealing algorithm (QAA). Such protocols will have numerous advantages over simple quantum annealing. By using such searches the effect of problem mis-specification can be reduced, as only energy differences between the searched states will be relevant. The QAA is an analogue of simulated annealing, a classical numerical technique which has now been superseded. Hence, I explore two strategies to use an annealer in a way which takes advantage of modern classical optimization algorithms. Specifically, I show how sequential calls to quantum annealers can be used to construct analogues of population annealing and parallel tempering which use quantum searches as subroutines. The techniques given here can be applied not only to optimization, but also to sampling. I examine the feasibility of these protocols on real devices and note that implementing such protocols should require minimal if any change to the current design of the flux qubit-based annealers by D-Wave Systems Inc. I further provide proof-of-principle numerical experiments based on quantum Monte Carlo that demonstrate simple examples of the discussed techniques.

Citation

Chancellor, N. (2017). Modernizing quantum annealing using local searches. New Journal of Physics, 19(2), Article 023024. https://doi.org/10.1088/1367-2630/aa59c4

• Journal Article Type: Article
• Acceptance Date: Jan 17, 2017
• Online Publication Date: Feb 10, 2017
• Publication Date: 2017
• Deposit Date: Feb 10, 2017
• Publicly Available Date: Feb 10, 2017
• Journal: New Journal of Physics
• Electronic ISSN: 1367-2630
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 2
• Article Number: 023024
• DOI: https://doi.org/10.1088/1367-2630/aa59c4
• Public URL: https://durham-repository.worktribe.com/output/1386585