Modernizing Quantum Annealing using Local Searches

TL;DR

This paper proposes using quantum annealers for local state space searches to improve optimization and sampling, integrating modern classical algorithms like population annealing and parallel tempering, with minimal hardware modifications.

Contribution

It introduces protocols for local quantum searches around specific states, enhancing quantum annealing's capabilities and combining it with classical optimization strategies.

Findings

Protocols feasible on current quantum annealers

Numerical experiments demonstrate effectiveness of local searches

Reduced impact of problem mis-specification

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. 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 quantum annealing algorithm is an analogue of simulated annealing, a classical numerical technique which has now been superseded.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.