# Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods

## TL;DR

This paper introduces a multi-start algorithm that enhances the performance of various samplers for optimization problems by fixing high-probability variables, significantly improving success metrics and scaling across multiple methods including quantum annealing.

## Contribution

The paper presents a general multi-start algorithm that improves sampler performance and scalability for optimization, demonstrated across quantum and classical methods.

## Key findings

- Success metrics and scaling are substantially improved.
- Quantum annealer's scaling is enhanced for Chimera graph problems.
- Parallel tempering solves larger 3D spin glass problems with the method.

## Abstract

We present and apply a general-purpose, multi-start algorithm for improving the performance of low-energy samplers used for solving optimization problems. The algorithm iteratively fixes the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are smaller and less connected, and samplers tend to give better low-energy samples for these problems. The algorithm is trivially parallelizable, since each start in the multi-start algorithm is independent, and could be applied to any heuristic solver that can be run multiple times to give a sample. We present results for several classes of hard problems solved using simulated annealing, path-integral quantum Monte Carlo, parallel tempering with isoenergetic cluster moves, and a quantum annealer, and show that the success metrics as well as the scaling are improved substantially. When combined with this algorithm, the quantum annealer's scaling was substantially improved for native Chimera graph problems. In addition, with this algorithm the scaling of the time to solution of the quantum annealer is comparable to the Hamze--de Freitas--Selby algorithm on the weak-strong cluster problems introduced by Boixo et al. Parallel tempering with isoenergetic cluster moves was able to consistently solve 3D spin glass problems with 8000 variables when combined with our method, whereas without our method it could not solve any.