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

Abstract

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

arXiv comment7 pages, 6 figures

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• Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis
• Variational approach to photonic quantum circuits via the parameter shift rule
• Synergic quantum generative machine learning
• Variational quantum cloning machine on an integrated photonic interferometer
• Programming multi-level quantum gates in disordered computing reservoirs via machine learning and TensorFlow
• Optical Quantum Computing