The Potential of Quantum Annealing for Rapid Solution Structure Identification

TL;DR

This paper explores the unique capabilities of quantum annealing in rapidly identifying solution structures in optimization problems, highlighting its potential advantages over classical methods.

Contribution

It provides new insights into quantum annealing's properties through carefully designed tasks and compares its performance to classical algorithms, suggesting hybrid approaches.

Findings

Quantum annealing can quickly identify high-quality solution structures.

Performance of quantum annealing is distinct from classical algorithms on specific tasks.

Potential for hybrid algorithms combining quantum and classical methods.

Abstract

The recent emergence of novel computational devices, such as quantum computers, coherent Ising machines, and digital annealers presents new opportunities for hardware-accelerated hybrid optimization algorithms. Unfortunately, demonstrations of unquestionable performance gains leveraging novel hardware platforms have faced significant obstacles. One key challenge is understanding the algorithmic properties that distinguish such devices from established optimization approaches. Through the careful design of contrived optimization tasks, this work provides new insights into the computation properties of quantum annealing and suggests that this model has the potential to quickly identify the structure of high-quality solutions. A meticulous comparison to a variety of algorithms spanning both complete and local search suggests that quantum annealing's performance on the proposed optimization tasks is distinct. This result provides new insights into the time scales and types of optimization problems where quantum annealing has the potential to provide notable performance gains over established optimization algorithms and suggests the development of hybrid algorithms that combine the best features of quantum annealing and state-of-the-art classical approaches.