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We report on experimental implementation of a machine-learned quantum gatedriven by a classical control. The gate learns optimal phase-covariant cloningin a reinforcement learning scenario having fidelity of the clones as reward.In our experiment, the gate learns to achieve nearly optimal cloning fidelityallowed for this particular class of states. This makes it a proof ofpresent-day feasibility and practical applicability of the hybrid machinelearning approach combining quantum information processing with classicalcontrol. Moreover, our experiment can be directly generalized to largerinterferometers where the computational cost of classical computer is muchlower than the cost of boson sampling.

Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner