Quantum Optimization Algorithms in Operations Research: Methods, Applications, and Implications

TL;DR

This paper reviews quantum optimization algorithms, comparing their techniques and potential impacts on decision-making in operations research, highlighting recent progress and future implications in the field.

Contribution

It provides a comprehensive overview of quantum optimization methods, their applications, and discusses their potential to revolutionize decision-making processes in operations research.

Findings

Quantum algorithms show promise for faster optimization solutions.

Adiabatic and gate-based algorithms have distinct advantages and limitations.

Quantum optimization could significantly impact key operations research areas.

Abstract

Quantum optimization algorithms (QOAs) have the potential to fundamentally transform the application of optimization methods in decision making. For certain classes of optimization problems, it is widely believed that QOA enables significant run-time performance benefits over current state-of-the-art solutions. With the latest progress on building quantum computers entering the industrialization stage, quantum-based optimization algorithms have become more relevant. The recent extreme increase in the number of publications in the field of QOA demonstrates the growing importance of the topic in both the academia and the industry. The objectives of this paper are as follows: (1) First, we provide insight into the main techniques of quantum-based optimization algorithms for decision making. (2) We describe and compare the two basic classes of adiabatic and gate-based optimization algorithms and argue their potentials and limitations. (3) Herein, we also investigate the key operations research application areas that are expected to be considerably impacted by the use of QOA in decision making in the future. (4) Finally, current implications arising from the future use of QOA from an operations research perspective are discussed.