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Physical Review A

Optimizing single-photon generation and storage with machine learning

Miao Cai, Yanqing Lu, Min Xiao, Keyu Xia

Published Nov 12, 2021• 10 citations
Publisher

Abstract

Single photons are at the heart of quantum information processing. The tasks of generating and storing single photons with arbitrary wave-packet shapes are crucial for building quantum networks, but they remain challenging. Here, we present a general machine learning (ML) algorithm with a self-adaptive process to optimize the control of a cavity-atom system for these tasks. This ML algorithm shows high efficiency and fidelity for both generation and storage of single photons. This ML-enhanced single-photon interface may pave the way for building flexible and reliable quantum networks because this ML algorithm can automatically adjust the quantum system according to single-photon wave functions in an ``intelligent'' way.

DOI
10.1103/physreva.104.053707

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A cavity coupling, a charged nanodot, and a fiber can act as a quantum interface, through which a stationary spin qubit and a flying photon qubit can be interconverted via a cavity-assisted Raman process. This Raman process can be made to generate or annihilate an arbitrarily shaped single-photon wave packet by pulse shaping the controlling laser field. This quantum interface forms the basis for many essential functions of a quantum network, including sending, receiving, transferring, swapping, and entangling qubits at distributed quantum nodes as well as a deterministic source and an efficient detector of a single-photon wave packet with arbitrarily specified shape and average photon number. Numerical study of errors from noise and system parameters on the operations shows high fidelity and robust tolerance.
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