Work

Scalable quantum neural networks by few quantum resources

Year: 2024

Type: article

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.

Source: International Journal of Quantum Information

DOI: 10.1142/s0219749924500187

Authors: Davide Pastorello, Enrico Blanzieri

Institutions: Istituto Nazionale di Fisica Nucleare, Trento Institute for Fundamental Physics And Applications, University of Bologna, University of Trento

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Award: G507880695