Topology-enhanced superconducting qubit networks for in-sensor quantum information processing
Year: 2025
Type: article
Abstract: We investigate the influence of topology on the magnetic response of inductively coupled superconducting flux-qubit networks. Using exact diagonalization methods and linear response theory, we compare the magnetic response of linear and cross-shaped array geometries, used as paradigmatic examples. We find that the peculiar coupling matrix in cross-shaped arrays yields a significant enhancement of the magnetic flux response compared to linear arrays, this network-topology effect arising from cooperative coupling among the central and the peripheral qubits. These results establish quantitative design criteria for function-oriented superconducting quantum circuits, with direct implications for advancing performance in both quantum sensing and quantum information processing applications. Concerning the latter, by exploiting the non-linear and high-dimensional dynamics of such arrays, we demonstrate their suitability for quantum reservoir computing technology. This dual functionality suggests a novel platform in which the same device serves both as a quantum-limited electromagnetic sensor and as a reservoir capable of signal processing, enabling integrated quantum sensing and processing architectures.
Source: Quantum Science and Technology
Authors: J Settino, G G Luciano, A Di Bartolomeo, P Silvestrini, M Lisitskiy, B Ruggiero, F Romeo
Institutions: Istituto Nazionale di Fisica Nucleare, Gruppo Collegato di Cosenza, University of Calabria, Universitat de Lleida, University of Salerno, University of Campania "Luigi Vanvitelli", Institute of Applied Science and Intelligent Systems, Superconducting and other Innovative Materials and Devices Institute, Istituto di Cibernetica “Eduardo Caianiello”, National Research Council, Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, Istituto Nazionale di Fisica Nucleare, Gruppo Collegato di Salerno
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,895
Funder: Universitat de Lleida
Award: G6838105669
OpenAlex ID: W4416450481
