Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks

Qianke Wang; Jun Liu; Dawei Lyu; Jian Wang

doi:10.1038/s41377-023-01336-7

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Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks

Original Articles

- Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks
- Light: Science & Applications Vol. 13, Issue 1, Pages: 55-67(2024)
- Affiliations：
  
  1.Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074 Hubei, China
  2.Optics Valley Laboratory, Wuhan 430074 Hubei, China
- Author bio：
  
  Jian Wang (jwang@hust.edu.cn)
- Funds：
- DOI：10.1038/s41377-023-01336-7
  CLC：
- Received：27 May 2023，
  
  Revised：30 October 2023，
  
  Accepted：2023-11-12，
  
  Published Online：05 January 2024，
  
  Published：31 January 2024
- Accepted：
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Wang, Q. K. et al. Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks.,Light: Science & Applications, 13, 55-67 (2024).
DOI：

Wang, Q. K. et al. Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks.,Light: Science & Applications, 13, 55-67 (2024). DOI： 10.1038/s41377-023-01336-7.

摘要

Abstract

While the spatial mode of photons is widely used in quantum cryptography

its potential for quantum computation remains largely unexplored. Here

we showcase the use of the multi-dimensional spatial mode of photons to construct a series of high-dimensional quantum gates

achieved through the use of diffractive deep neural networks (D

NNs). Notably

our gates demonstrate high fidelity of up to 99.6(2)%

as characterized by quantum process tomography. Our experimental implementation of these gates involves a programmable array of phase layers in a compact and scalable device

capable of performing complex operations or even quantum circuits. We also demonstrate the efficacy of the D

NN gates by successfully implementing the Deutsch algorithm and propose an intelligent deployment protocol that involves self-configuration and self-optimization. Moreover

we conduct a comparative analysis of the D

NN gate's performance to the wave-front matching approach. Overall

our work opens a door for designing specific quantum gates using deep learning

with the potential for reliable execution of quantum computation.

关键词

Keywords

references

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