Wang Qianke, Liu Jun, Lyu Dawei, Wang Jian
Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
Optics Valley Laboratory, Wuhan, 430074, Hubei, China.
Light Sci Appl. 2024 Jan 5;13(1):10. doi: 10.1038/s41377-023-01336-7.
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 (DNNs). 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 DNN 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 DNN 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.
虽然光子的空间模式在量子密码学中被广泛使用，但其在量子计算方面的潜力在很大程度上仍未得到探索。在这里，我们展示了利用光子的多维空间模式来构建一系列高维量子门，这是通过使用衍射深度神经网络（DNN）实现的。值得注意的是，我们的量子门表现出高达99.6(2)%的高保真度，这是通过量子过程层析成像来表征的。我们对这些量子门的实验实现涉及在一个紧凑且可扩展的设备中的可编程相位层阵列，该阵列能够执行复杂操作甚至量子电路。我们还通过成功实现德伊算法证明了DNN量子门的有效性，并提出了一种涉及自配置和自优化的智能部署协议。此外，我们对DNN量子门与波前匹配方法的性能进行了比较分析。总体而言，我们的工作为利用深度学习设计特定量子门打开了一扇门，具有可靠执行量子计算的潜力。
