Implementing evolutionary optimization on actual quantum processors

This paper introduces a new evolutionary algorithm with the support of an actual quantum processor, a computing device which uses phenomena from quantum mechanics to enable a considerable speed-up in computation. In particular, the proposed approach uses quantum superposition and entanglement to implement quantum evolutionary concepts such as quantum chromosome, entangled crossover, rotation mutation, and quantum elitism, to efficiently perform genetic evolution on quantum devices, and converge towards proper sub-optimal solutions of a given optimization problem. The proposed quantum genetic algorithm has been implemented by using a hybrid hardware architecture, where classical processors interact with the family of quantum processors provided by the IBM Q Experience (R) initiative. As shown in the experimental section, the proposed quantum genetic algorithm's performance highlights that the synergy between quantum and evolutionary computation results in a new and promising bio-inspired optimization strategy. (C) 2021 Elsevier Inc. All rights reserved.