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

作     者:Qianke Wang Jun Liu Dawei Lyu Jian Wang Qianke Wang;Jun Liu;Dawei Lyu;Jian Wang

作者机构:Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic InformationHuazhong University of Science and TechnologyWuhan430074HubeiChina Optics Valley LaboratoryWuhan430074HubeiChina 

出 版 物:《Light(Science & Applications)》 (光(科学与应用)(英文版))

年 卷 期:2024年第13卷第1期

页      面:55-67页

核心收录:

学科分类:08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 0702[理学-物理学] 

基  金:supported by the National Natural Science Foundation of China(62125503,62261160388,62001182,62371202) the Natural Science Foundation of Hubei Province of China(2023AFA028,2023AFB814) the Key R&D Program of Guangdong Province(2018B030325002) the Key R&D Program of Hubei Province of China(2021BAA024,2020BAB001) the Innovation Project of Optics Valley Laboratory(OVL2021BG004). 

主  题:quantum dimensional operations 

摘      要: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 (D2NNs). 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 D2NN 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 D2NN 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.

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