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Multiobjective Optimization Grover Adaptive Search

  • Benjamín Barán
  • Marcos VillagraEmail author
Chapter
  • 203 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 795)

Abstract

Quantum computing is a fast evolving subject with a promise of highly efficient solutions to difficult and complex problems in science and engineering. With the advent of large-scale quantum computation, a lot of effort is invested in finding new applications of quantum algorithms. In this article, we propose an algorithm based on Grover’s adaptative search for multiobjective optimization problems where access to the objective functions is given via two different quantum oracles. The proposed algorithm, considering both types of oracles, are compared against NSGA-II, a highly cited multiobjective optimization evolutionary algorithm. Experimental evidence suggests that the quantum optimization method proposed in this work is at least as effective as NSGA-II in average, considering an equal number of executions.

Keywords

Multiobjective Optimization Problem (MOP) Quantum Adiabatic Algorithm Grover Operator Oracle Performance Hypervolume Metric 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidad Nacional de Asunción, NIDTECSan LorenzoParaguay

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