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On Post-processing the Results of Quantum Optimizers

  • Ajinkya BorleEmail author
  • Josh McCarter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11934)

Abstract

The use of quantum computing for applications involving optimization has been regarded as one of the areas it may prove to be advantageous (against classical computation). To further improve the solutions, post-processing techniques are often used on the results of quantum optimization. One such recent approach is the Multi Qubit Correction (MQC) algorithm by Dorband. In this paper, we will discuss and analyze the strengths and weaknesses of this technique. Based on our discussion, we perform an experiment on (i) how pairing heuristics on the input of MQC can affect the results of a quantum optimizer and (ii) a comparison between MQC and the built-in optimization method that D-wave Systems offers. Among our results, we are able to show that the built-in post-processing rarely beats MQC in our tests. We hope that by using the ideas and insights presented in this paper, researchers and developers will be able to make a more informed decision on what kind of post-processing methods to use for their quantum optimization needs.

Keywords

Quantum optimization Quantum annealing Approximation Evolutionary algorithm D-wave QAOA 

Notes

Acknowledgments

We would like to thank John Dorband, Milton Halem and Samuel Lomonaco, Helmut Katzgraber and Nicholas Chancellor for their feedback. A special thanks to D-wave Systems for providing us access to their machines.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CSEE DepartmentUniversity of Maryland Baltimore CountyBaltimoreUSA

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