Efficient Depth Selection for the Implementation of Noisy Quantum Approximate Optimization Algorithm

Yu Pan, Yifan Tong, Shibei Xue, Guofeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with control depth. In principle, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by increasing the depth. In this work, we propose to use the regularized model selection algorithm to identify the optimal depth with just a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises.

Original languageEnglish
Pages (from-to)11273-11287
Number of pages15
JournalJournal of the Franklin Institute
Volume359
Issue number18
DOIs
Publication statusPublished - Dec 2022

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics

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