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

## TL;DR

This paper demonstrates a hybrid quantum-classical reinforcement learning approach to optimize quantum cloning fidelity using boson sampling, showing practical feasibility for quantum information processing.

## Contribution

It introduces an experimental method for training a quantum gate with classical control to perform near-optimal phase-covariant cloning, integrating machine learning with quantum hardware.

## Key findings

- Achieved nearly optimal cloning fidelity for phase-covariant states.
- Proved the feasibility of hybrid quantum-classical reinforcement learning.
- Suggested scalability to larger interferometers with reduced classical computational costs.

## 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.