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.
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.
Superconducting quantum sensors use flux quantization, the Josephson effect and low-loss microwave circuits to measure magnetic flux, microwave signals and photon flux close to fundamental limits. In this review we follow the field from the Bardeen–Cooper–Schrieffer theory and the discovery of Josephson tunnelling to SQUID magnetometers, microwave kinetic inductance detectors and quantum-limited parametric amplifiers. We explain how dc and rf SQUIDs, superconducting resonators, and Josephson and kinetic-inductance amplifiers work, and we describe one measurement chain that covers them all, with separate excitation, response, amplification, control and readout paths and a fluxlocked-loop variant for SQUIDs. Flux and field noise, noise-equivalent power, added noise, bandwidth and dynamic range are defined and compared using numbers taken directly from the primary literature, together with the conditions under which they were measured. We then survey applications in biomagnetism, astronomical imaging, qubit readout and dark-matter searches, and close with the open problems of scalability, environmental noise and fair benchmarking.
Superconducting double-loop interferometers (bi-SQUIDs) have been introduced to produce magnetic flux sensors specifically designed to exhibit an ultrahighly linear voltage response as a function of the magnetic flux. These devices are very important for quantum sensing and for signal processing of signals oscillating in the radio-frequency range of the electromagnetic spectrum. Here, we report an Al double-loop bi-SQUID based on proximitized mesoscopic Cu Josephson junctions. Such a scheme provides an alternative fabrication approach to conventional tunnel-junction-based interferometers, where the junction characteristics and, consequently, the magnetic-flux-to-voltage and magnetic-flux-to-critical-current device responses can be largely and easily tailored by the geometry of the metallic weak links. We discuss the performance of such sensors by showing a full characterization of the device switching current and voltage drop versus the magnetic flux for operation temperatures ranging from 30 mK to approximately 1K. The figures of merit of the transfer function and of the total harmonic distortion are also discussed. The latter provides an estimate of the linearity of the flux-to-voltage device response, which attains values as large as 45 dB. Such a result lets us foresee a performance already on par with that achieved in conventional tunnel-junction-based bi-SQUIDs arrays composed of hundreds of interferometers.
A measurement of a single-qubit quantum sensor reveals at most 1 bit of information about the signal that was sensed. To perform a classification task on the signal, the conventional approach is to repeat a sensing protocol many times, averaging the measurement results to obtain a high-precision estimate of the sensed signal, and then to apply classical postprocessing. Quantum computational sensing (QCS) is an alternative approach that breaks with the paradigm of first obtaining a classical estimate of the signal and then computing a function of the estimated signal in postprocessing. QCS instead combines quantum sensing with quantum computing to concentrate information about the signal relevant to the task into the solitary bit revealed by each measurement. Here, we report on the experimental demonstration of QCS where sensing and computing were both performed by the same single superconducting transmon qubit. We consider various binary classification tasks based on static and oscillating magnetic fields induced by current through a flux line. The fields were sensed through a double-junction superconducting quantum interference device (SQUID) loop that was part of the qubit. We used a protocol based on quantum signal processing to preprocess the sensed signals in the quantum domain prior to measurement. For tasks on static magnetic fields, our protocol outperformed the conventional baseline of Ramsey-based phase estimation by as much as 15 percentage points. For tasks on oscillating magnetic fields, we classified signal amplitude and frequency with up to 20 and 15 percentage points higher accuracy, respectively, compared to the conventional baseline of optimized dynamical-decoupling protocols. Our results illustrate how quantum computing can enhance quantum sensing even with a minimally sized quantum system subject to the practical limitations of decoherence and error-prone operations.
Advancements in the field of cavity quantum electrodynamics (c-QED) have deepened understanding of quantum phenomena, enabled reliable Josephson junction-based devices, and facilitated their integration with other quantum technologies to explore new physics and applications. Motivated by these developments, this thesis aims to construct a testbed for studying quantum thermodynamics using a superconducting flux qubit. The flux qubit is particularly suited for thermodynamic experiments due to its strong anharmonicity and its galvanic coupling architectures that facilitate efficient energy transport. In this thesis, the on-chip components include harmonic oscillators realized as superconducting resonators, a strongly nonlinear superconducting flux qubit, two copper thermal reservoirs modelled as Johnson–Nyquist noise sources and NIS thermometers, integrated on a highly resistive silicon platform that acts as a phonon bath. These components enable the study and optimization of heat transport between the metallic reservoirs, mediated by the quantum circuit, with the flux qubit as the central, flux-tunable working substance. Heat transport through the qubit occurs via sequential excitation and relaxation cycles between its ground and excited states, where excitation extracts heat equal to the qubit’s energy gap from the hot reservoir, while relaxation releases the same energy into the cold reservoir. Enhancing the thermal device’s performance critically depends on the coupling strength between on-chip components, particularly between the qubit and resonator. This thesis commences with the investigation and demonstration of a robust qubit–resonator coupling design that achieves the ultrastrong regime experimentally. Building on this result, the developed architecture is extended to connect the qubit with two identical resonators, symmetrically positioned on the two sides. These resonators are, in turn, coupled to metallic reservoirs, enabling the first ever demonstration of flux qubit-controlled heat transport between the two reservoirs with excellent device performance. Added geometric asymmetry in the coupler, combined with the flux qubit’s strong anharmonicity, enables the successful demonstration--presented in this thesis--of a non-reciprocal device such as the Microwave Quantum Diode, which, together with the reservoirs, could potentially form an efficient thermal diode. Finally, the thesis concludes with a brief outlook on future directions and ongoing experiments that build on the flux qubit–based testbed developed here for exploring quantum thermodynamic phenomena and device applications.
Crosstalk refers to unwanted qubit addressing. This is particularly detrimental when scaling up quantum information systems because unintended interactions limit their overall performance. For superconducting qubits, tunable couplings and frequency tunability achieved through externally applied magnetic fluxes enable high-fidelity entangling gates; however, they also introduce crosstalk through unintended flux coupling. In this work, we investigate the impact of time-dependent external magnetic fluxes in quantized circuits on superconducting qubit couplings. We find that non-trivial cross-voltage driving emerges between capacitively linked qubits when the magnetic flux threading the SQUID loop of a qubit varies in time, in a manner analogous to Faraday's law of induction. Crucially, we show that this effect enables fast single qubit control through the coupler element in standard tunable-coupler architectures, potentially eliminating the need for individual microwave XY control lines.
Quantum metrology based on superconducting qubits offers unprecedented precision in measuring fundamental physical quantities, particularly magnetic fields. In this study, we develop a modified quantum phase estimation algorithm utilizing superconducting qubits, enhanced by optimized initial state preparation, significantly improving measurement sensitivity and reducing estimation times compared to conventional techniques. To ensure practical applicability and robustness, we incorporate advanced noise modeling, including intrinsic decoherence effects such as flux-induced noise and Purcell losses. Through detailed analytical modeling and numerical simulations, we systematically evaluate sensitivity and practical implementation constraints across various superconducting qubit architectures—transmon, fluxonium, conventional flux, and C-shunt flux qubits. Our approach quantifies the critical trade-offs between coherence, measurement dynamics, and sensitivity, identifying optimal operational regimes for high-performance quantum sensing. We introduce custom circuit simulation methods enabling the analysis and optimization of arbitrary superconducting quantum devices. Our findings provide a framework for advancing quantum-enhanced magnetometry and promise to facilitate next-generation quantum sensors for fundamental physics research, gravitational field measurements, and quantum-assisted searches for dark matter candidates.