Quantum Annealing for Industry Applications: Introduction and Review

TL;DR

This paper reviews the development, hardware, software, and applications of quantum annealing technology, highlighting its potential and limitations for solving complex optimization problems across industries.

Contribution

It provides a comprehensive overview of quantum annealing, including theoretical background, technological requirements, and recent practical applications, serving as a centralized resource.

Findings

Quantum annealing is effective for combinatorial optimization problems.

Recent advances have enabled practical applications in industry.

Limitations include hardware scalability and problem-specific performance.

Abstract

Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the quantum annealing algorithm for programmable use. Specifically, quantum annealing processors produced by D-Wave Systems have been studied and tested extensively in both research and industrial settings across different disciplines. In this paper we provide a literature review of the theoretical motivations for quantum annealing as a heuristic quantum optimization algorithm, the software and hardware that is required to use such quantum processors, and the state-of-the-art applications and proofs-of-concepts that have been demonstrated using them. The goal of our review is to provide a centralized and condensed source regarding applications of quantum annealing technology. We identify the advantages, limitations, and potential of quantum annealing for both researchers and practitioners from various fields.