Topology-Enhanced Superconducting Qubit Networks for In-Sensor Quantum Information Processing

Jerry M. Chow

Jacopo Settino, Giuseppe Gaetano Luciano, Antonio Di Bartolomeo +4 more

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.

Adriel I. Santoso, Le Bin Ho

Dark matter (DM) remains one of the most compelling unresolved problems in fundamental physics, motivating the search for new detection approaches. We propose a network-based quantum sensor architecture to enhance sensitivity to ultralight DM fields. Each node in the network is a superconducting qubit, interconnected via controlled-Z gates in symmetric topologies such as line, ring, star, and fully connected graphs. We investigate four- and nine-qubit systems, optimizing both state preparation and measurement using a variational quantum metrology framework. This approach minimizes the quantum and classical Cramér-Rao bounds to identify optimal configurations. Bayesian inference is employed to extract the DM-induced phase shift from measurement outcomes. Our results show that optimized network configurations significantly outperform conventional Greenberger-Horne-Zeilinger-based protocols while maintaining shallow circuit depths compatible with noisy intermediate-scale quantum hardware. Sensitivity remains robust under local dephasing noise. These findings highlight the importance of network structure in quantum sensing and point toward scalable strategies for quantum-enhanced DM detection.

Stefan Filipp, Sarah Sheldon, Easwar Magesan +4 more

Andrey R. Klots, R. McDermott, L. B. Ioffe

Jincheng Zhang

This paper proposes and investigates the design and theoretical framework for a quantum sensor network leveraging the principles of quantum coherence. The core claim is the construction of a network capable of achieving high-precision sensing through enhanced signal amplification facilitated by quantum coherence. The fundamental mechanism involves exploiting quantum coherence to amplify the strength of the measured signal and subsequently establishing a network for multi-dimensional monitoring of physical quantities. This approach represents a novel sensing paradigm, shifting from classical sensitivity limitations to quantum-enhanced performance. We explore the theoretical underpinnings of this network, focusing on coherence preservation strategies, signal processing techniques, and network topology considerations. The presented model outlines a potential architecture for a sensor network employing entangled qubits, demonstrating the feasibility of achieving sensitivities far beyond the limits of conventional sensors. Further research and development, including the practical implementation of such a network, are crucial steps towards realizing the full potential of quantum sensing.