Latest Papers

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.

Controlling the transport and nature of quantum excitations in low-dimensional systems is a key requirement for scalable quantum devices, including communication networks and quantum simulators. We propose a one-dimensional hybrid quantum lattice model, in which each lattice unit integrates a single-mode resonator that interacts with a two-level system (TLS), featuring direct coupling between adjacent TLSs. This configuration enables the coherent propagation of excitations with tunable atomic, photonic, or polaritonic character. Beyond conventional single-excitation transport, we demonstrate that appropriate impedance-matching and resonance conditions allow for the controlled swapping of excitation type as the excitation propagates along the lattice. We analyze the resulting dynamics using local observables and pairwise concurrence to track both transport and quantum correlations. Our results establish a minimal platform for controlled single-excitation conversion, with direct relevance to hybrid quantum networks, on-chip quantum interconnects, and engineered quantum simulators.

Reducing materials and processing-induced decoherence is critical to the development of utility-scale quantum processors based on superconducting qubits. Here we report on the impact of two fluorine-based wet etches, which we use to treat the silicon surface underneath the Josephson junctions (JJs) of fixed-frequency transmon qubits made with aluminum base metallization. Using several materials analysis techniques, we demonstrate that these surface treatments can remove germanium residue introduced by our JJ fabrication with no other changes to the overall process flow. These surface treatments result in significantly improved energy relaxation times for the highest performing process, with median $T_1=334~μ$s, corresponding to quality factor $Q=6.6\times10^6$. This result suggests that the metal-substrate interface directly underneath the JJs was a major contributor to microwave loss in these transmon qubit circuits prior to integration of these surface treatments. Furthermore, this work illustrates how materials analysis can be used in conjunction with quantum device performance metrics to improve performance in superconducting qubits.

We operationally uncover aspects of wave/particle duality for the open driven Jaynes-Cummings (JC) model in its strong-coupling limit. We lay special emphasis on the vacuum Rabi resonance, and determine the corresponding normalized intensity-field correlation function via mapping to ordinary resonance fluorescence. We demonstrate that temporal wave-particle fluctuations of light emanating from an established vacuum Rabi resonance are explicitly non-classical, while the limit of vanishing spontaneous emission restores detailed balance. When photon blockade sets in, individual realizations show a rapidly increasing frequency of fluctuations towards the two-photon resonance, arising as a direct consequence of a resolved JC spectrum. About the two-photon resonance peak, spontaneous emissions are more likely to revive a high-frequency quantum beat in the conditioned electromagnetic field amplitude than photons escaping out of the cavity mode. The beat originates from a coherent superposition of the first excited JC couplet states, and sets the background against which nonclassical phase shifts are observed in the conditioned quadrature amplitudes. We also find that the onset of steady-state bimodality reduces the variation of the normalized intensity-field correlation, at the expense of its temporal symmetry.

In this paper, we investigate the influence of spatial curvature on the Jaynes-Cummings model. We employ an analog model of general relativity, representing the field inside a cavity using oscillators arranged in a circle instead of a straight line, where increasing curvature corresponds to a smaller circle radius. We investigate the nonclassical features of this quantum system arising from the interaction between a two-level atom and a deformed harmonic oscillator on a circle, which serves as curved-space counterpart to the flat oscillator. We analyze the time evolution of atom-field states and, based on this dynamic, examine how spatial curvature influences the Mandel parameter, entropy, and the behavior of the Wigner distribution function. Our results demonstrate that the spatial curvature plays critical roles in controlling these nonclassical properties.

Artificial atoms non-locally coupled to waveguides -- the so-called giant atoms -- offer new opportunities for the control of light and matter. In this work, we show how to use an array of non-locally coupled transmon "molecules" to engineer a passive photonic controlled gate for waveguide photons. In particular, we show that a conditional elastic phase shift between counter-propagating photons arises from the interplay between direction-dependent couplings, engineered through an interplay of non local interactions and molecular binding strength; and the nonlinearity of the transmon array. We analyze the conditions under which a maximal $π$-phase shift -- and hence a CZ gate -- is obtained, and characterize the gate fidelity as a function of key experimental parameters, including finite transmon nonlinearities, emitter spectral inhomogeneities, and limited cooperativity. Our work opens the use of giant atoms as key elements of microwave photonic quantum computing devices.

Efficient qubit reset and leakage reduction are essential for scalable superconducting quantum computing, particularly in the context of quantum error correction. However, such operations often require additional on-chip components. Here, we propose and experimentally demonstrate a hardware-efficient approach to qubit reset and readout using a multi-mode Purcell filter in a superconducting quantum circuit. We exploit the inherent multi-mode structure of a coplanar waveguide resonator, using its fundamental and second-order modes for qubit reset and readout, respectively, thereby avoiding additional components. Implemented in a flip-chip architecture, our device achieves unconditional reset with residual excitation below 1\% in 220 ns, and a leakage reduction unit that selectively resets the second excited state within 62 ns with a residual $|f\rangle$ population of 6.1\%, accounting for the readout error. Despite the qubits being directly coupled to the filter in our configuration, the measured relaxation times are not degraded owing to intrinsic Purcell protection provided by an auxiliary mode. To our knowledge, this is the first experimental trial that exploits different-order modes of a microwave resonator for distinct qubit operations, representing a new direction toward scalable, hardware-efficient quantum processor design.

The field of superconducting qubits is constantly evolving with new circuit designs. However, when it comes to qubit readout, the use of simple transverse linear coupling remains overwhelmingly prevalent. This standard readout scheme has significant drawbacks: in addition to the Purcell effect, it suffers from a limitation on the maximal number of photons in the readout mode, which restricts the signal-to-noise ratio (SNR) and the Quantum Non-Demolition (QND) nature of the readout. Here, we explore the high-power regime by engineering a nonlinear coupling between a transmon qubit and its readout mode. Our approach builds upon previous work by Dassonneville et al. [Physical Review X 10, 011045 (2020)], on qubit readout with a non-perturbative cross-Kerr coupling in a transmon molecule. We demonstrate a readout fidelity of 99.21% with 89 photons utilizing a parametric amplifier. At this elevated photon number, the QND nature remains high at 96.7%. Even with up to 300 photons, the QNDness is only reduced by a few percent. This is qualitatively explained by deriving a critical number of photons associated with the nonlinear coupling, yielding a theoretical value of $\bar{n}_r^\text{crit} = 377$ photons for our sample's parameters. These results highlight the promising performance of the transmon molecule in the high-power regime, establishing it as a compelling platform for high-fidelity qubit readout.

We propose and validate a framework for analog simulation of the Heisenberg spin model using a circuit quantum electrodynamics (circuit-QED) platform. To this end, we develop a continuous family of deformed boson representations of the SU(2) algebra, which includes the Holstein-Primakoff and Dyson-Maleev transformations as special cases. For spin-1/2 systems, we introduce a procedure to circumvent the inherent non-Hermiticity of the representation, showing that this entire family yields the extended Bose-Hubbard (EBH) Hamiltonian. For the experimental realization of this EBH model, we design a scalable circuit-QED architecture based on an engineered Josephson junction array. Numerical simulations confirm that the microwave photon dynamics in this simulator accurately reproduces the original spin dynamics. Our work establishes an experimentally accessible method for investigating complex quantum spin dynamics in a highly controllable bosonic setting.

Obtaining accurate representations of the eigenstates of an array of coupled superconducting qubits is a crucial step in the design of circuit quantum electrodynamics (QED)-based quantum processors. However, exact diagonalization of the device Hamiltonian is challenging for system sizes beyond tens of qubits. Here, we employ a variant of the density matrix renormalization group (DMRG) algorithm, DMRG-X, to efficiently obtain localized eigenstates of a 2D transmon array without the need to first compute lower-energy states. We also introduce MTDMRG-X, a new algorithm that combines DMRG-X with multi-target DMRG to efficiently compute excited states even in regimes with strong eigenstate hybridization. We showcase the use of these methods for the analysis of long-range couplings in a multi-transmon Hamiltonian including qubits and couplers, and we discuss eigenstate localization. These developments facilitate the design and parameter optimization of large-scale superconducting quantum processors.

In this paper, we address the phase space formulation of the Jaynes-Cummings model through the explicit construction of the full Wigner function for a hybrid bipartite quantum system composed of a two-level atom and a quantized coherent field. By employing the Stratonovich-Weyl correspondence and the coadjoint orbit method, we derive an informationally complete quasi-probability distribution that captures the full dynamics of light-matter interaction. This approach provides a detailed phase space perspective of fundamental quantum phenomena such as Rabi oscillations, atomic population inversion, and entanglement generation. We further measure the purity of the reduced quantized field state by means of an appropriate Wigner function corresponding to the bosonic field part in order to investigate the entanglement dynamics of the system.

In this paper, we describe some interesting properties of a non-Hermitian Jaynes-Cummings model. For this particular model, it is known that the Hilbert space can be described by infinitely-many two-dimensional invariant (closed) subspaces, together with the global ground state. We expose the appearance of exceptional points on such two-dimensional subspaces, together with quantum phase transitions marking the transition from real to complex eigenvalues. We also compute the spin-oscillator entanglement entropy on each invariant subspace to show that the two phases can be distinguished by their distinct entanglement-entropy profiles.

Quantum reservoir computing is a machine learning framework that offers ease of training compared to other quantum neural networks, as it does not rely on gradient-based optimization. Learning is performed in a single step on the output features measured from the quantum system. Various implementations of quantum reservoir computing have been explored in simulations, with different measured features. Although simulations have shown that quantum reservoirs present advantages in performance compared to classical reservoirs, experimental implementations have remained scarce. This is due to the challenge of obtaining a large number of output features that are nonlinear transformations of the input data. In this work, we show that even with a circuit quantum electrodynamics system as simple as a single transmon coupled to a readout resonator, we can implement a proof-of-concept realization of quantum reservoir computing. We obtain a large number of nonlinear features from a single physical system by encoding the input data in the amplitude of a coherent drive and measuring the cavity state in the Fock basis. We demonstrate classification of two classical tasks with significantly smaller hardware resources and fewer measured features compared to classical neural networks. Our experimental results are supported by numerical simulations that show additional Kerr nonlinearity is beneficial to reservoir performance. Our work demonstrates a hardware-efficient quantum neural network implementation that can be further scaled up and generalized to other quantum machine learning models.

Transmon qubits have traditionally been regarded as limited to random circuit sampling, incapable of performing Fock state boson sampling, a problem known to be classically intractable. This work challenges that assumption by introducing $q$ boson Fock state sampling, a variant in which transmon qubits can operate effectively. Through direct mapping to the $q$ boson formalism, we demonstrate that transmons possess the capability to achieve quantum supremacy in $q$ boson sampling tasks. This finding expands the potential applications of transmon-based quantum processors and paves the way for new avenues in quantum computation.

Superconducting quantum circuits provide a versatile platform for studying quantum materials by leveraging precise microwave control and utilizing the tools of circuit quantum electrodynamics (QED). Hybrid circuit devices incorporating novel quantum materials could also lead to new qubit functionalities, such as gate tunability and noise resilience. Here, we report experimental progress towards a transmon-like qubit made with a superconductor-topological insulator-superconductor (S-TI-S) Josephson junction using exfoliated BiSbTeSe2. We present a design that enables us to systematically characterize the hybrid device, from DC transport of the S-TI-S junction, to RF spectroscopy, to full circuit QED control and measurement of the hybrid qubit. In addition, we utilize a high-quality-factor superconducting cavity to characterize material and fabrication-induced losses, thereby guiding our efforts to improve device quality.

One of the roadblocks towards the implementation of a fault-tolerant superconducting quantum processor is impacts of ionizing radiation with the qubit substrate. Such impacts temporarily elevate the density of quasiparticles (QPs) across the device, leading to correlated qubit error bursts. The most damaging errors, $T_1$ errors, stem from QP tunneling across the qubit Josephson junctions (JJs). Recently, we demonstrated that this type of error can be strongly suppressed by engineering the profile of superconducting gap at the JJs in a way that prevents QP tunneling. In this work, we identify a new type of impact-induced correlated error that persists in the presence of gap engineering. We observe that impacts shift the frequencies of the affected qubits, and thus lead to correlated phase errors. The frequency shifts are systematically negative, reach values up to $3\,{\rm MHz}$, and last for $\sim 1\,{\rm ms}$. We provide evidence that the shifts originate from QP-qubit interactions in the JJ region. Further, we demonstrate that the shift-induced phase errors can be detrimental to the performance of quantum error correction protocols.

We present a dry surface treatment combining atomic layer etching and deposition (ALE and ALD) to mitigate dielectric loss in fully fabricated superconducting quantum devices formed from aluminum thin films on silicon. The treatment, performed as a final processing step prior to device packaging, starts by conformally removing the native metal oxide and fabrication residues from the exposed surfaces through ALE before \textit{in situ} encapsulating the metal surfaces with a thin dielectric layer using ALD. We measure a two-fold reduction in loss attributed to two-level system (TLS) absorption in treated aluminum-based resonators and planar transmon qubits. Treated transmons with compact capacitor plates and gaps achieve median $Q$ and $T_1$ values of $3.69 \pm 0.42 \times 10^6$ and $196 \pm 22$~$μ$s, respectively. These improvements were found to be sustained over several months. We discuss how the combination of ALE and ALD reverses fabrication-induced surface damages to significantly and durably improve device performance via a reduction of the TLS defect density in the capacitive elements.

Quantum coherence, encoded in the off-diagonal elements of a system's density matrix, is a key resource in quantum thermodynamics, fundamentally limiting the maximum extractable work known as ergotropy. While previous experiments have isolated coherence-related contributions to work extraction, it remains unclear how coherence can be harnessed in a controllable and energy-efficient manner. Here, we experimentally investigate the role of initial-state coherence in work extraction from a superconducting transmon qubit. By preparing a variety of pure states and implementing three complementary extraction protocols, we reveal how coherence governs the partitioning of ergotropy. We find that the choice of initial state depends on the dominant decoherence channel-energy relaxation or dephasing. By further accounting for thermodynamic costs, we identify optimal initial states that maximize the efficiency. Our results demonstrate that the initial-state design provides a scalable approach to coherence control and advances the development of efficient quantum thermodynamic devices.

Motivated by recent developments in the field of multilevel quantum batteries, we present the model of a quantum device for energy storage with anharmonic level spacing, based on a superconducting circuit in the transmon regime. It is charged via the sequential interaction with a collection of identical and independent ancillary two-level systems. By means of a numerical analysis we show that, in case these ancillas are coherent, this kind of quantum battery can achieve remarkable performances for what it concerns the control of the stored energy and its extraction in regimes of parameters within reach in nowadays quantum circuits.

Quantum processors require rapid and high-fidelity simultaneous measurements of many qubits. While superconducting qubits are among the leading modalities toward a useful quantum processor, their readout remains a bottleneck. Traditional approaches to processing measurement data often struggle to account for crosstalk present in frequency-multiplexed readout, the preferred method to reduce the resource overhead. Recent approaches to address this challenge use neural networks to improve the state-discrimination fidelity. However, they are computationally expensive to train and evaluate, resulting in increased latency and poor scalability as the number of qubits increases. We present an alternative machine learning approach based on next-generation reservoir computing that constructs polynomial features from the measurement signals and maps them to the corresponding qubit states. This method is highly parallelizable, avoids the costly nonlinear activation functions common in neural networks, and supports real-time training, enabling fast evaluation, adaptability, and scalability. Despite its lower computational complexity, our reservoir approach is able to maintain high qubit-state-discrimination fidelity. Relative to traditional methods, our approach achieves error reductions of up to 50% and 11% on single- and five-qubit datasets, respectively, and delivers up to 2.5x crosstalk reduction on the five-qubit dataset. Compared with recent machine-learning methods, evaluating our model requires 100x fewer multiplications for single-qubit and 2.5x fewer for five-qubit models. This work demonstrates that reservoir computing can enhance qubit-state discrimination while maintaining scalability for future quantum processors.