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

• Published in: Light, science & applications science & applications, 2024-01, Vol.13 (1), p.10-10, Article 10
• Main Authors: Wang, Qianke, Liu, Jun, Lyu, Dawei, Wang, Jian
• Format: Article
• Language: English
• Subjects: 639/624/400/3925; 639/624/400/482; Comparative analysis; Cryptography; Deep learning; Lasers; Microwaves; Neural networks; Optical and Electronic Materials; Optical Devices; Optics; Photonics; Photons; Physics; Physics and Astronomy; RF and Optical Engineering
• ISSN: 2047-7538; 2047-7538
• 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 (D 2 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 2 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 2 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. We showcase the use of deep diffractive neural networks to construct a series of high fidelity and high-dimensional quantum gates, which are encoded by the spatial modes of photons.