We study the behavior of a single laser-driven trapped ion inside a microscopic optical Fabry-Perot cavity. In particular, we demonstrate a fiber Fabry-Perot cavity operating on the principal S 1/2 → P 1/2 electric dipole transition of an Yb + ion at 369 nm with an atom-ion coupling strength of g = 2π × 67(1) MHz. We employ the cavity to study the generation of single photons and observe cavity-induced back-action in the Purcell-enhanced emission of photons. Tuning of the amplitude and phase of the back-action allows us to enhance or suppress the total rate of photoemission from the ion-cavity system.

The development of efficient network nodes is a key element for the realization of quantum networks which promise great capabilities as distributed quantum computing or provable secure communication. We report the realization of a quantum network node using a trapped ion inside a fiber-based Fabry–Perot cavity. We show the generation of deterministic entanglement at a high fidelity of 90.1(17)% between a trapped Yb ion and a photon emitted into the resonator mode. We achieve a success probability for generation and detection of entanglement for a single shot of 2.5 × 10−3 resulting in 62 Hz entanglement rate.

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We introduce the use of two machine learning algorithms to create an empirical model of an experimental apparatus, which is able to reduce the number of measurements necessary for generic optimisation tasks exponentially as compared to unbiased systematic optimisation. Principal Component Analysis (PCA) can be used to reduce the degrees of freedom in cases for which a rudimentary model describing the data exists. We further demonstrate the use of an Artificial Neural Network (ANN) for tasks where a model is not known. This makes the presented method applicable to a broad range of different optimisation tasks covering multiple fields of experimental physics. We demonstrate both algorithms at the example of detecting and compensating stray electric fields in an ion trap and achieve a successful compensation with an exponentially reduced amount of data.

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