Gradient-based Quantum Neural Network Using Quantum Computing Chips

Y. C. Zhan, H. Zhang, H. Cai, D. P. Poenar, L. C. Kwek, A. Q. Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

An analytic gradient algorithm for training neural networks in an 8-mode quantum photonic chip is proposed. The algorithm shows 4x faster-training speed and 3% accuracy rate enhancement than existing approaches in solving two classification tasks.

Original languageEnglish
Title of host publicationCLEO
Subtitle of host publicationApplications and Technology, CLEO:A and T 2023
PublisherOptical Society of America
ISBN (Electronic)9781957171258
DOIs
Publication statusPublished - May 2023
Externally publishedYes
EventCLEO: Applications and Technology, CLEO:A and T 2023 - Part of Conference on Lasers and Electro-Optics 2023 - San Jose, United States
Duration: 7 May 202312 May 2023

Publication series

NameCLEO: Applications and Technology, CLEO:A and T 2023

Conference

ConferenceCLEO: Applications and Technology, CLEO:A and T 2023 - Part of Conference on Lasers and Electro-Optics 2023
Country/TerritoryUnited States
CitySan Jose
Period7/05/2312/05/23

ASJC Scopus subject areas

  • General Computer Science
  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Space and Planetary Science
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Instrumentation

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