An Overview of Approaches to Modernize Quantum Annealing Using Local Searches

TL;DR

This paper explores how quantum annealers can be adapted to perform local searches around specific states, leveraging modern classical optimization techniques to improve robustness and effectiveness over traditional quantum annealing methods.

Contribution

It introduces strategies for using quantum annealers for local searches, integrating modern classical optimization approaches to enhance performance and reduce sensitivity to problem mis-specification.

Findings

Quantum annealers can be adapted for local search tasks.

Local search strategies may be less sensitive to problem mis-specification.

Potential improvements over traditional quantum annealing methods.

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. The quantum annealing algorithm is an analogue of simulated annealing, a classical numerical technique which is now obsolete. Hence, I explore strategies to use an annealer in a way which takes advantage of modern classical optimization algorithms, and additionally should be less sensitive to problem mis-specification then the traditional quantum annealing algorithm.