Correlated optical convolutional neural network with "quantum speedup"

Yifan Sun; Qian Li; Ling-Jun Kong; Xiangdong Zhang

doi:10.1038/s41377-024-01376-7

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Correlated optical convolutional neural network with "quantum speedup"

Original Articles

- Correlated optical convolutional neural network with "quantum speedup"
- Light: Science & Applications Vol. 13, Issue 2, Pages: 333-346(2024)
- Affiliations：
  
  Key Laboratory of advanced optoelectronic quantum architecture and measurements of Ministry of Education, Beijing Key Laboratory of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology, 100081 Beijing, China
- Author bio：
  
  Xiangdong Zhang (zhangxd@bit.edu.cn)
- Funds：
- DOI：10.1038/s41377-024-01376-7
  CLC：
- Received：05 July 2023，
  
  Revised：22 December 2023，
  
  Accepted：2023-12-31，
  
  Published Online：31 January 2024，
  
  Published：29 February 2024
- Accepted：
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Sun, Y. F. et al. Correlated optical convolutional neural network with "quantum speedup". Light: Science & Applications, 13, 333-346 (2024).
DOI：

Sun, Y. F. et al. Correlated optical convolutional neural network with "quantum speedup". Light: Science & Applications, 13, 333-346 (2024). DOI： 10.1038/s41377-024-01376-7.

摘要

Abstract

Compared with electrical neural networks

optical neural networks (ONNs) have the potentials to break the limit of the bandwidth and reduce the consumption of energy

and therefore draw much attention in recent years. By far

several types of ONNs have been implemented. However

the current ONNs cannot realize the acceleration as powerful as that indicated by the models like quantum neural networks. How to construct and realize an ONN with the quantum speedup is a huge challenge. Here

we propose theoretically and demonstrate experimentally a new type of optical convolutional neural network by introducing the optical correlation. It is called the correlated optical convolutional neural network (COCNN). We show that the COCNN can exhibit "quantum speedup" in the training process. The character is verified from the two aspects. One is the direct illustration of the faster convergence by comparing the loss function curves of the COCNN with that of the traditional convolutional neural network (CNN). Such a result is compatible with the training performance of the recently proposed quantum convolutional neural network (QCNN). The other is the demonstration of the COCNN's capability to perform the QCNN phase recognition circuit

validating the connection between the COCNN and the QCNN. Furthermore

we take the COCNN analog to the 3-qubit QCNN phase recognition circuit as an example and perform an experiment to show the soundness and the feasibility of it. The results perfectly match the theoretical calculations. Our proposal opens up a new avenue for realizing the ONNs with the quantum speedup

which will benefit the information processing in the era of big data.

关键词

Keywords

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