Runtime-coherence trade-offs for hybrid SAT-solvers

TL;DR

This paper investigates the balance between runtime and coherence time in hybrid quantum-classical algorithms for k-SAT, proposing a trade-off relation that guides how to partition tasks for limited coherence quantum computers.

Contribution

It introduces a formal trade-off relation between runtime and coherence time for hybrid quantum algorithms solving k-SAT, and analyzes specific partition strategies to optimize this balance.

Findings

Identifies a fundamental trade-off between runtime and coherence time in hybrid algorithms.

Explicitly determines the trade-off relations for certain partition schemes.

Numerical simulations indicate additional flexibility in achieving optimal trade-offs.

Abstract

Many search-based quantum algorithms that achieve a theoretical speedup are not practically relevant since they require extraordinarily long coherence times, or lack the parallelizability of their classical counterparts.This raises the question of how to divide computational tasks into a collection of parallelizable sub-problems, each of which can be solved by a quantum computer with limited coherence time. Here, we approach this question via hybrid algorithms for the k-SAT problem. Our analysis is based on Sch\"oning's algorithm, which solves instances of k-SAT by performing random walks in the space of potential assignments. The search space of the walk allows for "natural" partitions, where we subject only one part of the partition to a Grover search, while the rest is sampled classically, thus resulting in a hybrid scheme. In this setting, we argue that there exists a simple trade-off relation between the total runtime and the coherence-time, which no such partition based hybrid-scheme can surpass. For several concrete choices of partitions, we explicitly determine the specific runtime coherence-time relations, and show saturation of the ideal trade-off. Finally, we present numerical simulations which suggest additional flexibility in implementing hybrid algorithms with optimal trade-off.