Latest Papers

Interactions of cosmic rays and other forms of ionizing radiation pose a significant challenge to the reliable operation of state-of-the-art quantum devices and error correction in quantum computing based on superconducting circuits which are typically fabricated on semiconductor substrates. Shielded by 2 km of rock overburden, the Cryogenic Underground TEst facility (CUTE) at SNOLAB provides a unique ultra-low radiation environment to probe the performance of quantum technologies with a particular interest in quantum coherence studies. In this article, we present the findings of an extensive material assaying program in preparation for the first underground operation of superconducting qubits at SNOLAB. The radioactivity levels identified by the material assays enter a thorough Monte Carlo study based on the Geant4 particle physics tracking code. From these simulations, we estimate the rates of energy deposits from radiogenic sources expected for a quantum-device assembly operated in the CUTE facility. We further characterize the spectral components of the projected background and identify the dominant particle interaction types. Finally, we outline how crystal dynamics simulations using the G4CMP solid-state physics extension for Geant4 can inform the community-wide efforts to identify effective strategies to mitigate the effects of high-energy particle impacts.

Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications. Current detection methods operate offline, introducing latency incompatible with in-the-loop qubit control. In this paper, an online detector of charge jumps for superconducting qubits, based on a dilated causal convolutional neural network (DCCNN) designed for in-the-loop deployment on the Quantum Instrumentation Control Kit (QICK) platform, is presented. The network is trained on synthetic Ramsey tomography scans generated from qubit templates measured at the Northwestern Experimental Underground Site (NEXUS) at Fermilab, and translated to FPGA firmware via hls4ml with ap_fixed$\langle 16,6 \rangle$ quantization, reaching a per-inference latency of $6.19 μ$s on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline $χ^2$ algorithm ($0.843 \pm 0.022$ vs. $0.866 \pm 0.020$ on $|Δq| \in [0.1, 0.5] e$ at matched false-positive rate), while requiring no per-qubit hyperparameter tuning. This shifts charge-jump detection from a post-hoc diagnostic to a control-loop primitive, enabling adaptive protocols that respond to radiation-induced events in situ, with applications to quantum-computing error mitigation and to the use of superconducting qubits as particle detectors.

A solid-state quantum-computing architecture will require the fabrication of arrays of many coupled qubits. It is inevitable that this process will produce qubits and couplers with varying performance, with some components underperforming due to imperfect fabrication. Quantum error-correction requires high-performing components and hence these defects must be dealt with, either by adapting the code to exclude the defects, or by informing the decoder to accommodate defects in post-processing. Here we implement and compare strategies to operate distance-5 surface codes on a quantum processor consisting of a square-lattice array of 120 superconducting qubits. We demonstrate a dramatic reduction in the probability of a logical error in a memory experiment by excluding underperforming components, compared with both a standard approach of ignoring defects, and a defect-aware decoding approach. We observe up to 2.8X improvement in logical errors per round when excluding defects compared with the standard defect-ignorant approach (1.62% compared to 4.49%). In contrast, defect-aware decoding gives only modest gains. Defects are also expected to be particularly harmful for measurement-based logical operations. Using a stability experiment we show that excluding defects resurrects measurement-based logic gate performance, observing a 6.3% per-round suppression of failure rate when excluding defects, compared to zero suppression otherwise. Furthermore, we show a further substantial decrease in logical errors when using leakage post-selection in combination with our defect exclusion strategies, resulting in a distance-5 code outperforming the best distance-3 in one basis. Our experiments therefore give a proof-of-principle demonstration of the essential utility of defect exclusion methods in the scale-up of solid-state quantum computing approaches.

Reliable qubit readout is a critical bottleneck toward fault-tolerant quantum computing (FTQC). In superconducting quantum processors, readout operations are both error-prone and high-latency. These challenges become more severe in frequency-multiplexed architectures, where signal crosstalk among neighboring qubits significantly degrades readout fidelity. Existing machine learning (ML)-based approaches rely on feed-forward neural networks (FNNs) that suffer from large parameter sizes and lack an end-to-end network that jointly addresses relaxation errors and discriminates qubit states. In this work, we present a multi-stage qubit state discriminator based on the Mamba model, which enables efficient sequence modeling with linear complexity. The first stage performs initial state discrimination, followed by a refinement stage that identifies and mitigates relaxation-induced errors. Our lightweight model achieves a geometric mean readout fidelity of 0.906, outperforming the best-reported state-of-the-art method while reducing parameter size by 49.6%; our optimal model further reaches 0.911. Both models remain robust across varying input trace lengths, maintaining a high fidelity of 0.893 at readout durations as short as 500 $ns$, achieving up to a 26% reduction in logical error rate over prior work in quantum error correction (QEC).

Gate-based digital quantum simulations offer an exciting new paradigm for studying the many-body physics of strongly correlated systems. In this context, electron-phonon models are challenging for qubit-only quantum simulators, as bosonic degrees of freedom require costly finite-dimensional encodings. Here, we elaborate on an alternative approach based on a digital-analog circuit QED architecture, where fermions are encoded in transmon qubits while bosons are represented directly by microwave resonators. The central building block of this framework is a qubit-resonator Rabi gate that emulates strong electron-phonon coupling and can be implemented through a sequence of resonant Jaynes-Cummings gates interleaved with layers of single-qubit rotations. Using this Rabi gate as the fundamental unitary operation, we construct quantum circuits for the Hubbard-Holstein and Yukawa-Sachdev-Ye-Kitaev models, which describe, respectively, strongly correlated electrons coupled to phonons and phonon-mediated interactions among Majorana fermions. We further demonstrate how nonclassical phonon physics and signatures of quantum chaos in these models can be probed through circuit simulations, and develop measurement and variational protocols tailored to near-term superconducting quantum hardware.

The multi-qubit gates fidelity of superconducting quantum processors can be limited due to the dressing of computational states by noncomputational ones. Here, we experimentally and analytically investigate a transmon-transmon-bus system where the computational states dressing is tunable over a broad range. We estimate the dressing using three methods: a full three-element model, an effective mode approach, and an unperturbed mode approach. The obtained results highlight the importance of the accurate estimation and control of the computational states dressing in order to optimize gates on superconducting platform.

Recent advances in design and fabrication have increased the energy-relaxation times of isolated superconducting transmon qubits to the hundreds-of-microseconds regime, with reported values exceeding 500 $μ$s. However, the same progress has not automatically translated to multiqubit processors, where qubits are embedded in connected qubit-coupler lattices and often exhibit much shorter lifetimes than isolated qubits. To identify possible sources of this discrepancy, here we use finite-element simulation to investigate how surface participation ratios and the resulting surface dielectric loss change when a qubit is embedded in a flip-chip qubit-coupler lattice. Controlled comparisons show that higher connectivity can indeed lead to larger surface loss: in the simulated lattice, connecting a qubit to two and four couplers increases the surface loss by factors of 1.3 and 1.8, respectively. We attribute this change to the combined effects of added edge fields from coupling claws, field redistribution over the larger connected metal network, and hybridization with coupler modes. We further examine how this connectivity-induced surface-loss penalty depends on the geometric design parameters of both the qubit electrodes and the coupling claws, and derive guidelines for designing low-loss multiqubit processors.

We propose a novel formulation of master equations for open systems wherein the evolution of a state is determined solely by its local behaviour at any point of time. Specifically, our formulation allows for a local interpretation of the workings of beyond-Markovian dynamics as opposed to the more common conception that non-Markovian state evolution is affected by its cumulative past history. Quite interestingly, local determinacy is found prevalent in quantum dynamics. We illustrate the advantages of our coordinate-free formulation with exact analyses on two physically relevant models.

We introduce a hybrid qubit-boson beam-splitter gate in which a microwave cavity mode couples to an exchange-dressed two-level subsystem of an interacting two-qubit system in the presence of Kerr nonlinearity. Starting from a general circuit quantum electrodynamics (cQED) model, we derive the corresponding hybrid qubit-cavity interaction, develop its open-system description including photon- and qubit-sector-bath-induced dissipation and obtain in the weak-dissipation regime an analytical expression for the average gate fidelity. We further identify carbon-nanotube circuit QED as a concrete platform for implementing and controlling the gate, provide a representative operating regime and perform noiseless and noisy numerical simulations to study the gate dynamics and benchmark the analytical results. Beyond this implementation route, the proposed hybrid primitive provides a natural building block for many-body dynamics, including quantum-cellular-automaton (QCA) and lattice-gauge-inspired architectures and, through its collision-model reformulation, also suggests connections to noisy QCA, non-Markovian extensions and reservoir-style quantum information processing.

Accurate knowledge of the on-chip temperature is essential for understanding and optimizing the performance of superconducting qubits, yet direct thermometry at millikelvin temperatures remains challenging. While qubits themselves are sensitive to the temperature of their environment, other factors may affect the qubits` effective temperature, and using them as thermometers with any accuracy requires specialized measurement protocols and qubit designs, limiting their practicality for routine diagnostics and adding complex infrastructure to any hardware testing apparatus. Here we demonstrate a complementary on-chip thermometry method based on superconducting Dayem bridges that are integrated on the same chip as transmon qubits. By extracting the critical current of the Dayem bridge from I-V measurements, we obtain a local, quantitative measure of the chip temperature without the need for microwave calibration or qubit-specific control sequences. To demonstrate the utility of the Dayem bridges as thermometers, we fabricate them in-situ with qubits on the same chip, calibrate the Dayem bridge critical current as a function of temperature, and characterize its resolution and stability at cryogenic temperatures. We additionally perform simultaneous measurements of the Dayem bridge thermometer and qubit excited-state population, and show agreement over the relevant temperature range, validating the method against established qubit thermometry. Furthermore, we correlate the independently measured chip temperature with qubit energy relaxation and dephasing times, demonstrating the utility of this approach for diagnosing temperature-dependent decoherence mechanisms. These results establish integrated Dayem bridges as a simple, non-invasive, and scalable tool for cryogenic hardware development, and on chip thermometry in superconducting quantum circuits.

Leakage to noncomputational states limits the speed of single-qubit gates in weakly anharmonic transmons. Conventional pulse-shaping methods, including derivative removal by adiabatic gate (DRAG), primarily suppress the leakage remaining at the end of a gate. Here we demonstrate that endpoint leakage and the transient leakage population that accumulates during the gate represent distinct control objectives. Endpoint leakage is associated with the drive spectrum at the anharmonicity, whereas transient exposure depends on spectral weight over a finite frequency band and governs the additional leakage induced by dephasing. A spectral null at the leakage transition therefore suppresses the endpoint amplitude without necessarily reducing transient exposure. Based on this distinction, we introduce a path--endpoint separation pulse that combines transient-path shaping with a two-tone endpoint correction. For a $10$ ns $R_X(π/2)$ gate with an anharmonicity magnitude of $0.2$ GHz, numerical simulations show a $21\%$ reduction in transient exposure relative to cosine DRAG and a corresponding $20\%$ reduction in dephasing-induced excess leakage. The two correction tones further suppress residual leakage through the $|2\rangle$ and $|3\rangle$ channels, lowering the coherent endpoint leakage from approximately $7\times10^{-7}$ to $3\times10^{-8}$ without increasing transient exposure. These results establish transient exposure and endpoint leakage as complementary targets for the design of fast transmon gates.

We introduce a superconducting digital-to-analog converter (DAC) that filters control noise, provides native multiplexing, performs quantum gates in nanoseconds, and can be controlled by CMOS. This is achieved by transducing a trapezoidal drive pulse into a superconducting soliton, which is then held in the DAC load loop, applying flux to a mutually-coupled superconducting qubit or gate coupler. The analog flux output by the DAC can be easily controlled by varying the soliton hold time, or with a DC-biased tunable DAC-qubit coupler, allowing the DAC to perform a fixed-time, high-fidelity gate that's robust to fabrication variance or flux offsets in the quantum circuit. Our initial demonstration shows that the DAC can successfully perform 5.6 ns S-gates on transmons. We measure the DAC-induced quantum state excitation probability per gate to be 0.05%, and find that the DAC-induced relaxation rate from the qubit 1 state is below the intrinsic T1 rate limit of the transmon. Quantum simulations show qualitative agreement with the measured data, and predict that the DAC excitation rate can be lowered 10 times further by overdamping the Josephson junction (JJ) in the DAC load loop. may be limited by a Interleaved Randomized Benchmarking (IRB) sequences on an observer qubit reveal that, when scaling to many qubits, the DAC's performance may be limited by a non-local, DAC-induced phase error of 1.6% per gate, appearing in ancilla qubits that are not directly coupled to any of the 30 DACs on the chip. We discuss strategies for future layouts of multi-DAC chips that focus on mitigating the source of these non-local, high-frequency electromagnetic interactions (EMI), and how to incorporate a DC-tunable coupler for phase correction.

Calibrating a superconducting transmon chip is a sequential decision problem under noise, drift, and a finite budget: an expert must choose experiments, read ambiguous plots, judge fit quality, and revise stale beliefs as the chip drifts. We study whether a vision-language agent can close this loop and specialize itself to one physical device without weight updates, via three co-designed artifacts. The first is a physics-grounded simulation environment for transmon chips: calibration observables derive from circuit-quantized parameters via scqubits, with realistic flux-line distortion, wall-time-scaled and mid-scan drift, and gate leakage, concerns a toy simulator would omit; each tool call advances a modeled clock so drift accrues by wall time, not call count. The second is a vision-language agent that runs the loop end to end, calling tools, reading plots, maintaining a structured notebook, and submitting parameters without hidden truth, scored against hidden parameters and gate fidelities measured on the device. The third is gradient-free online adaptation: a reflector reads truth-free anomaly signatures from past attempts and grows a small, human-readable device note appended to the prompt, admitted by a paired-snapshot accept gate that isolates strategy improvement from drift. On a hard-tier chip under budget pressure, six iterations raised the worst-case CZ fidelity from 0.678 to 0.787 and cut its variance, reproducing at four-qubit scale; a single accepted note raised CZ fidelity from 0.678 to 0.913 on its paired snapshot. A planted-fault study confirms the note is causal, diagnosing a hardware fault truth-free, its principal value raising the failure floor and cutting variance. The agent, scoring, and reward transfer to real hardware via a measurement-backend swap; only the accept gate is a simulation affordance, reducing to a held-out-slice or repeat-and-average form.

Three-dimensional superconducting radio-frequency (SRF) cavities provide exceptionally long-lived electromagnetic modes and, when coupled to nonlinear elements such as transmon qubits, become promising architectures for bosonic quantum information processing. The inverse design of such systems, i.e., recovering device geometries that produce specified electromagnetic and coupling targets, is generally a one-to-many problem. The qubit-cavity coupling strength depends sensitively on both the transmon geometry and its position within the cavity's electromagnetic field. As these systems scale up and their design parameter spaces grow, the cost of conventional iterative simulation becomes prohibitive. We present two deep neural network (DNN) approaches that address this inverse-design problem at complementary levels of the design stack. The first proposes SRF cavity geometries that produce target cavity observables. The second proposes transmon qubit designs that produce target qubit-cavity parameters - the coupling rate, qubit frequency, and anharmonicity $(g, ν_q, α)$. The recovered candidate designs match the targets to within ~5% (cavity) and ~2% (transmon), confirmed by end-to-end re-simulation. Both approaches map desired device behavior directly to candidate designs, a fast alternative to the iterative simulation studies usually required.

Qubit leakage is a noticeable source of errors for quantum computing. In quantum processors, leakage excitations traveling between qubits generate correlated errors and perturb gate implementations. Leakage mobility can also be utilized for creating dedicated leakage removal pathways and removal units. To quantitatively characterize leakage mobility and to guide better design of processor architectures, we study here leakage dynamics in transmons with tunable couplers through numerical and analytical methods. Even if the couplers are tuned to cancel the single-excitation exchange or the ZZ interaction, the leakage hopping rates still persists in the range of 0.8-10 MHz due to transmon nonlinearity. In typical operation regimes, however, transmon frequency detuning localizes leakage excitations. The next-nearest-neighbor transmons can be still be near-resonant opening leakage tunneling channels. To suppress longer-range hopping, we find that the frequency spread of the next-nearest-neighbor transmons needs to be in the range of 1-4 MHz. Utilizing leakage mobility, we propose two passive leakage removal units. One is based on a tunable coupler and a pumped transmon, and another on a junction readout scheme. Based on realistic experimental parameters, our results on selectively mobilizing or localizing leakage excitations are readily applicable in superconducting quantum devices.

Superconducting quantum chips commonly utilize quarter-wavelength (λ/4) transmission line resonators as readout circuits. An analytical model for the accurate determination of resonance frequencies and coupling Q-factors of feedline-coupled superconducting resonators is introduced. The model leverages four-port microwave network analysis, integrating boundary conditions and conformal mapping techniques to compute even- and odd-mode impedances in edge-coupled coplanar waveguide (CPW) structures. Its versatility allows application to both planar and 3-D heterogeneous architectures, making it a powerful tool for resonator design. To validate the model, a test chip with λ/4 resonators of varying geometries is fabricated and measured in a cryogenic environment. Comparisons with finite element method (FEM) simulations and experimental measurements confirm the model's accuracy, with resonance frequencies and coupling Q-factors aligning closely across configurations. This proposed model facilitates the design of superconducting resonators in readout circuits for more effective, scalable, and adaptable quantum computing architectures.

Superconducting qubits are a leading platform for quantum computing. However, simultaneously achieving low noise sensitivity to suppress decoherence and sufficient anharmonicity to enable fast gate operations remains a central challenge. Here, we introduce the concept of the ferroelectric transmon (FEmon), in which the Josephson junction is shunted by a ferroelectric, or incipient ferroelectric, capacitor. We show, in particular, that the nonlinear ferroelectric response of the capacitor provides an additional degree of freedom for optimizing qubit anharmonicity while preserving operation in the charge-noise-insensitive regime.

Superconducting transmon systems are promising platforms for nanoscale thermometry due to their high sensitivity to environmental fluctuations. Their intrinsic anharmonicity, which is essential for qubit operations, gives rise to a non-equidistant energy spectrum that significantly affects the thermal populations and, consequently, the thermometric sensitivity. In this work, we investigate the ultimate quantum limits of temperature estimation with a transmon beyond the two-level approximation. We compare the thermometric performance of three complementary models: the qubit, a harmonic oscillator and a weakly anharmonic Duffing oscillator, evaluating their corresponding quantum Fisher information (QFI) as a function of the temperature. We show that the multilevel anharmonic structure of the transmon affects its thermometric precision. Indeed, including higher excited states enhances the maximum amount of information that can be extracted about the system temperature, compared to a qubit probe. Furthermore, we address a fundamental limitation of the standard quartic truncation, which yields a potential that is unbounded from below and supports only spurious metastable states. By introducing bounded anharmonic models, namely a confined quartic potential and a sextic correction term, we assess the robustness of the thermometric precision beyond the Duffing regime. Our results provide practical guidelines for transmon-based nanoscale thermometry and clarify the role of the anharmonic multilevel spectrum in quantum temperature estimation.

Superconducting transmon processors represent a leading platform for large-scale quantum computing due to their high gate fidelities and scalability. However, conventional qubit-coupler-qubit (QCQ) architectures face critical physical and structural bottlenecks, notably frequency crowding [spectator qubit collisions] during system scaling and inefficient mapping onto the standard surface code.To overcome these limitations, we propose a novel lattice-patch architecture that couples four fixed-frequency transmons to a single fixed-frequency coupler.This design enhances qubit connectivity and maps directly onto the surface-code lattice unit [plaquette], thereby minimizing the compilation overhead associated with logical qubit implementation. Furthermore, utilizing an entirely fixed-frequency design intrinsically eliminates susceptibility to external flux noise, ensuring robust operational stability.Multi-level numerical simulations demonstrate CNOT gate fidelities exceeding 0.98 across all six connectivity directions within the patch. Nevertheless, the complex interaction network of the four-qubit architecture induces unintended residual phase accumulation during cross-resonance driving. This parasitic effect necessitates precise calibration, achievable via virtual $R_z$ gates [software phase updates]. Ultimately, our results establish the lattice-patch architecture as an efficient, robust building block for future fault-tolerant quantum computers.

Low-noise parametric amplifiers are widely used as the first-stage amplifier in qubit readout chains. The performance of parametric amplifiers depends sensitively on the choice of the pump condition. We propose a strategy for determining the pump condition that is tailored for fast multiplexed readout. Choosing the amplifier pump to maximize the signal-to-noise ratio (SNR) improvement at the readout frequency of the limiting qubit--the qubit that requires the longest readout time to reach a target SNR--minimizes the total multiplexed readout time. We demonstrate our pump calibration strategy experimentally on a five-qubit multiplexed readout chain with a traveling-wave parametric amplifier. Using our strategy, we reduce the multiplexed readout time by 320 ns compared to optimizing the average SNR improvement on all qubits, without degrading the target SNR for any qubit.