Abstract
AbstractWhile 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.
Publisher
Springer Science and Business Media LLC
Subject
Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Reference61 articles.
1. Nielsen, M. A. & Chuang, I. L. Quantum Computation and Quantum Information. (Cambridge: Cambridge University Press, 2010).
2. Barends, R. et al. Superconducting quantum circuits at the surface code threshold for fault tolerance. Nature 508, 500–503 (2014).
3. Xu, Y. et al. High-fidelity, high-scalability two-qubit gate scheme for superconducting qubits. Phys. Rev. Lett. 125, 240503 (2020).
4. Ballance, C. J. et al. High-fidelity quantum logic gates using trapped-ion hyperfine qubits. Phys. Rev. Lett. 117, 060504 (2016).
5. Srinivas, R. et al. High-fidelity laser-free universal control of trapped ion qubits. Nature 597, 209–213 (2021).