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

TL;DR

This paper demonstrates high-fidelity, high-dimensional quantum gates using diffractive deep neural networks, enabling complex quantum operations with scalable, programmable photonic devices, and showcases their application in quantum algorithms.

Contribution

It introduces a novel method of constructing high-dimensional quantum gates with deep learning-based diffractive neural networks, achieving high fidelity and scalability.

Findings

Quantum gates with up to 99.6% fidelity

Successful implementation of the Deutsch algorithm

Comparison shows advantages over wave-front matching

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 (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.