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.
Pastorello, D., Blanzieri, E. (2024). Scalable quantum neural networks by few quantum resources. INTERNATIONAL JOURNAL OF QUANTUM INFORMATION, 0, 1-16 [10.1142/s0219749924500187].
Scalable quantum neural networks by few quantum resources
Pastorello, Davide;
2024
Abstract
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.File in questo prodotto:
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