Quantum Brain
← Back to papers

Evaluation of Quantum and Hybrid Solvers for Combinatorial Optimization

A. Bertuzzi, Davide Ferrari, Antonio Manzalini, Michele Amoretti·March 15, 2024·DOI: 10.1145/3649153.3649205
Computer SciencePhysics

AI Breakdown

Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.

Abstract

Academic and industrial sectors have been engaged in a fierce competition to develop quantum technologies, fueled by the explosive advancements in quantum hardware. While universal quantum computers have been shown to support up to hundreds of qubits, the scale of quantum annealers has reached three orders of magnitude (i.e., thousands of qubits). Therefore, quantum algorithms are becoming increasingly popular in a variety of fields, with optimization being one of the most prominent. This work aims to explore the topic of quantum optimization by comprehensively evaluating the technologies provided by D-Wave Systems. To do so, a model for the energy optimization of data centers is proposed as a benchmark. D-Wave quantum and hybrid solvers are compared, in order to identify the most suitable one for the considered application. To highlight its advantageous performance capabilities and associated solving potential, the selected D-Wave hybrid solver is then contrasted with CPLEX, a highly efficient classical solver.

Related Research

Quantum Intelligence

Ask about quantum research, companies, or market developments.