Scalable quantum neural networks by few quantum resources
Davide Pastorello, Enrico Blanzieri · International Journal of Quantum Information · 2024
This paper focuses on the construction of a general parametric model that can be implemented executing multiple swap tests over few qubits and applying a suitable measurement protocol. The model turns out to be equivalent to a two-layer feedforward neural network which can be realized combining small quantum modules. The advantages and the perspectives of the proposed quantum method are discussed.