Multi-stage quantum walks for finding Ising ground states

TL;DR

This paper introduces an efficient heuristic for multi-stage quantum walks to improve the approximation of quantum annealing schedules, demonstrating polynomial scaling for easy problems and exponential scaling for harder ones.

Contribution

Develops a heuristic for selecting parameters in multi-stage quantum walks, enhancing their performance over single-stage methods for optimization problems.

Findings

Heuristic improves scaling for easy problems with large energy gaps.

Scaling becomes exponential for harder problems as stages increase.

Method is general and applicable to various optimization problems.

Abstract

One way to approximate a quantum annealing schedule is to use multiple quantum walks chained together, without intermediate measurements, to produce a multi-stage quantum walk (MSQW). Previous work has shown that MSQW is better than QAOA (quantum alternating operator ansatz) for solving optimization tasks using multiple stages [Gerblich et al, arXiv:2407.06663]. In this work, we develop an efficient heuristic for choosing the free parameters in MSQW, and use it to obtain improved scaling compared to single stage quantum walks. We show numerically that the heuristic works well for easy problems with a large minimum energy gap, giving a scaling polynomial in the number of stages, leading to an overall algorithm that scales polynomially in time. For harder problems, the scaling breaks down such that adding more stages decreases the success probability, leading to an overall scaling that is exponential in time, as expected. Our methods are general and can be applied to any optimization problem to obtain good annealing schedules.