Comparing algorithms for graph isomorphism using discrete- and continuous-time quantum random walks

TL;DR

This paper analytically compares discrete- and continuous-time quantum random walks in their ability to distinguish strongly regular graphs, revealing that discrete-time walks with multiple particles are more powerful for this task.

Contribution

It provides the first analytical demonstration of the distinguishing capabilities of quantum walks, highlighting differences between discrete- and continuous-time approaches.

Findings

Discrete-time multi-particle quantum walks can distinguish some strongly regular graphs.

Continuous-time quantum walks cannot distinguish all strongly regular graphs.

Subtle differences in graph certificate algorithms affect the distinguishing power.

Abstract

Berry and Wang [Phys. Rev. A {\bf 83}, 042317 (2011)] show numerically that a discrete-time quantum random walk of two noninteracting particles is able to distinguish some non-isomorphic strongly regular graphs from the same family. Here we analytically demonstrate how it is possible for these walks to distinguish such graphs, while continuous-time quantum walks of two noninteracting particles cannot. We show analytically and numerically that even single-particle discrete-time quantum random walks can distinguish some strongly regular graphs, though not as many as two-particle noninteracting discrete-time walks. Additionally, we demonstrate how, given the same quantum random walk, subtle differences in the graph certificate construction algorithm can nontrivially impact the walk's distinguishing power. We also show that no continuous-time walk of a fixed number of particles can distinguish all strongly regular graphs when used in conjunction with any of the graph certificates we consider. We extend this constraint to discrete-time walks of fixed numbers of noninteracting particles for one kind of graph certificate; it remains an open question as to whether or not this constraint applies to the other graph certificates we consider.