Improving Quantum Annealing Performance on Embedded Problems

Michael R. Zielewski, Mulya Agung, Ryusuke Egawa, Hiroyuki Takizawa

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, many researchers have been investigating quantum annealing as a solver for realworld combinatorial optimization problems. However, due to the format of problems that quantum annealing solves and the structure of the physical annealer, these problems often require additional setup prior to solving. We study how these setup steps affect performance and provide insight into the interplay among them using the job-shop scheduling problem for our evaluation. We show that the empirical probability of success is highly sensitive to problem setup, and that excess variables and large embeddings reduce performance.We then show that certain problem instances are unable to be solved without the use of additional post-processing methods. Finally, we investigate the effect of pausing during the anneal. Our results show that pausing within a certain time window can improve the probability of success, which is consistent with other work. However, we also show that the performance improvement due to pausing can be masked depending on properties of the embedding, and thus, special care must be taken for embedded problems.

Original languageEnglish
Pages (from-to)32-48
Number of pages17
JournalSupercomputing Frontiers and Innovations
Volume7
Issue number4
DOIs
Publication statusPublished - 2020

Keywords

  • combinatorial optimization
  • job-shop scheduling
  • quantum annealing
  • quantum computer

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics

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