Gradient-based Quantum Neural Network Using Quantum Computing Chips

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

doi:10.1364/CLEO_AT.2023.JTh2A.22

CLEO 2023
Technical Digest Series (Optica Publishing Group, 2023),
paper JTh2A.22
•https://doi.org/10.1364/CLEO_AT.2023.JTh2A.22

Gradient-based Quantum Neural Network Using Quantum Computing Chips

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

CLEO: Applications and Technology 2023

San Jose, CA United States
7–12 May 2023
ISBN: 978-1-957171-25-8

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Joint Poster Session III (JTh2A)

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Y. C. Zhan, H. Zhang, H. Cai, D. P. Poenar, L. C. Kwek, and A. Q. Liu, "Gradient-based Quantum Neural Network Using Quantum Computing Chips," in CLEO 2023, Technical Digest Series (Optica Publishing Group, 2023), paper JTh2A.22.

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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.

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