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.
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