Seeking Quantum Speedup Through Spin Glasses: The Good, the Bad, and the Ugly

TL;DR

This paper explores strategies to design challenging spin-glass benchmark instances to better evaluate quantum annealing devices and assess potential quantum advantage over classical algorithms.

Contribution

It proposes a new benchmarking approach comparing algorithm performance across different problem classes and introduces a methodology to evaluate quantum advantage beyond speedup detection.

Findings

Performance hit increases with problem complexity

Quantum annealing shows competitive results on certain instances

Methodology may clarify quantum advantage potential

Abstract

There has been considerable progress in the design and construction of quantum annealing devices. However, a conclusive detection of quantum speedup over traditional silicon-based machines remains elusive, despite multiple careful studies. In this work we outline strategies to design hard tunable benchmark instances based on insights from the study of spin glasses - the archetypal random benchmark problem for novel algorithms and optimization devices. We propose to complement head-to-head scaling studies that compare quantum annealing machines to state-of-the-art classical codes with an approach that compares the performance of different algorithms and/or computing architectures on different classes of computationally hard tunable spin-glass instances. The advantage of such an approach lies in having to only compare the performance hit felt by a given algorithm and/or architecture when the instance complexity is increased. Furthermore, we propose a methodology that might not directly translate into the detection of quantum speedup, but might elucidate whether quantum annealing has a "`quantum advantage" over corresponding classical algorithms like simulated annealing. Our results on a 496 qubit D-Wave Two quantum annealing device are compared to recently-used state-of-the-art thermal simulated annealing codes.