Deterministic optimal quantum cloning via a quantum-optical neural network

Quantum machine learning represents a promising approach to disclose novel possibilities for quantum information protocols. Thus, recent efforts have been dedicated to determine how to merge, or extend, machine learning approaches in the quantum domain. A relevant example is provided by a quantum-optical neural network (QONN), a class of physical systems that aim to represent the quantum-optical version of classical neural networks. This architecture was first introduced to identify the structure of optical circuits in analogy to their classical counterpart, showing the possibility of tackling different quantum tasks. However, the full potential of such an approach still needs to be explored. In this work, we provide a step forward in this direction by employing it to tackle the fundamental task of optical quantum cloning. The employed architecture has been successfully trained to perform deterministic universal quantum cloning on a photonic platform, achieving results that are very close to that of an optimal universal quantum cloner. Furthermore, we have verified numerically the robustness of this approach with respect to experimental imperfections relevant for future implementations. This work thus shows the capabilities and flexibility of QONNs, which might prove important for designing even more complex optical circuits.