Latest Papers

In recent years, quantum simulators of topological models have been extensively studied across a variety of platforms and regimes. A new promising research direction makes use of meta-atoms with multiple intrinsic degrees of freedom, which to date have been predominantly studied in the classical regime. Here, we propose a superconducting quantum simulator to study an extension of the well-known "Zig-Zag" model with long-range cross-polarization couplings using polarization transmons hosting degenerate dipole orbitals. We map the phase transitions of the extended "Zig-Zag" model both numerically and analytically using inverse participation ratios and topological invariants. We demonstrate the existence of in-gap localized trivial and Tamm edge states. With linearized meta-atoms, we show via electromagnetic modeling that the proposed arrangement closely reproduces the extended "Zig-Zag" model. This work paves the way towards experimental investigation of the previously inaccessible topological quantum many-body phenomena.

Superconducting protected qubits aim to achieve sufficiently low error rates so as to allow realization of error-corrected, utility-scale quantum computers. A recent proposal encodes a protected qubit in the quasicharge degree of freedom of the conventional transmon device. Operating such a protected `quasicharge qubit' requires implementing new strategies. Here we show that an electronically-controllable tunnel junction formed by two topological superconductors can be used to implement single- and two-qubit gates on quasicharge qubits. Schemes for both these gates are based on the same dynamical $4π$-periodic Josephson effect and therefore have the same gate times and error characteristics. We simulate the dynamics of a topological Josephson junction in a parameter regime with non-negligible charging energy, and characterize the robustness of such gate operations against charge noise. Our results point to a compelling strategy for implementation of quasicharge qubit gates based on junctions of minimal Kitaev chains of quantum dots.

Superconducting qubits are among the most promising platforms for realizing practical quantum computers. One requirement to create a quantum processor is nonlinearity, which in superconducting circuits is typically achieved by sandwiching a layer of aluminum oxide between two aluminum electrodes to form a Josephson junction. These junctions, however, face several limitations that hinder their scalability: the small superconducting gap of aluminum necessitates millikelvin operating temperatures, the material interfaces lead to dissipation, and the sandwich geometry adds unwelcome capacitance for high-frequency applications. In this work, we address all three limitations using a novel superconducting weak link based on the superconductor-insulator transition. By locally thinning a single film of niobium nitride, we exploit its thickness-driven superconductor-insulator transition to form a weak link employing only atomic layer deposition and atomic layer etching. We utilize our weak links to produce a transmon qubit, '$planaron$', with a measured anharmonicity of $α/2π= 235$ MHz; at present, the linewidth is $κ/2π= 15 \mathrm{\: MHz}$. The high superconducting gap of niobium nitride can enable operation at elevated temperatures in future devices, and the fully planar geometry of the weak link eliminates superfluous material interfaces and capacitances. The investigation of small patches of material near the SIT can shed new light on the nature of the transition, including the role of dissipation and finite-size effects.

As atom-cavity systems are becoming more sophisticated, the limitations of the Jaynes-Cummings model are becoming more apparent. In this paper, we therefore take a more dynamical approach to the modelling of atom-cavity systems and do not reduce the electromagnetic field inside the resonator to a single mode. Our approach shows that the decay rate Gamma_cav of an emitter inside a subwavelength cavity with metallic mirrors can be much larger than its free space decay rate Gamma_free due to constructive interference effects of the emitted light. In general, however, we find that Gamma_cav = Gamma_free to a very good approximation which might explain why atom-cavity experiments with planar mirrors have not been able to operate in the so-called strong coupling regime.

Quantum thermometry plays a critical role in the development of low-temperature sensors and quantum information platforms. In this work, we propose and theoretically analyze a hybrid circuit quantum electrodynamics architecture in which a superconducting qubit is dispersively coupled to two distinct bosonic modes: one initialized in a weak coherent state and the other coupled to a thermal environment. We show that the qubit serves as a sensitive readout of the probe mode, mapping the interference between thermal and coherent photon-number fluctuations onto measurable dephasing. This mechanism enables enhanced sensitivity to sub-millikelvin thermal energy fluctuations through Ramsey interferometry. We derive analytic expressions for the qubit coherence envelope, compute the quantum Fisher information for temperature estimation, and demonstrate numerically that the presence of a coherent reference amplifies the qubit's sensitivity to small changes in thermal photon occupancy. Our results establish a new paradigm for quantum-enhanced thermometry and provide a scalable platform for future calorimetric sensing in high-energy physics and quantum metrology.

Hamiltonian inverse engineering enables the design of protocols for specific quantum evolutions or target state preparation. Perfect state transfer (PST) and remote entanglement generation are notable examples, as they serve as key primitives in quantum information processing. However, Hamiltonians obtained through conventional methods often lack robustness against noise. Assisted by inverse engineering, we begin with a noise-resilient energy spectrum and construct a class of Hamiltonians, referred to as the dome model, that significantly improves the system's robustness against noise, as confirmed by numerical simulations. This model introduces a tunable parameter $m$ that modifies the energy-level spacing and gives rise to a well-structured Hamiltonian. It reduces to the conventional PST model at $m=0$ and simplifies to a SWAP model involving only two end qubits in the large-$m$ regime. To address the challenge of scalability, we propose a cascaded strategy that divides long-distance PST into multiple consecutive PST steps. Our work is particularly suited for demonstration on superconducting qubits with tunable couplers, which enable rapid and flexible Hamiltonian engineering, thereby advancing the experimental potential of robust and scalable quantum information processing.

In superconducting quantum circuits, decoherence errors in qubits constitute a critical factor limiting quantum gate performance. To mitigate decoherence-induced gate infidelity, rapid implementation of quantum gates is essential. Here we propose a scheme for rapid controlled-Z (CZ) gate implementation through energy-level engineering, which leverages Rabi oscillations between the $\left|11\right\rangle$ state and the non-computational state in a tunable-coupler architecture. Numerical simulations achieved a $\mathrm{22~ns}$ nonadiabatic CZ gate with fidelity over $99.99\%$. We further investigated the performance of the CZ gate in the presence of anharmonicity offsets. The results demonstrate that a high-fidelity CZ gate with an error rate below $10^{-4}$ remains achievable even with finite anharmonicity variations. Furthermore, the detrimental impact of spectator qubits in different quantum states on the fidelity of CZ gate is effectively suppressed by incorporating a tunable coupler. This scheme exhibits potential for extending the circuit execution depth constrained by coherence time limitations.

Within a numerically exact simulation technique, the dissipative dynamics of a two-qubit architecture is considered in which each qubit couples to its individual noise source (reservoir). The goal is to reveal the role of subtle qubit-reservoir correlations including non-Markovian processes as a prerequisite to guide further improvements of quantum computing devices. This paper addresses the following three topics. First, we examine the validity of the rotating wave approximation imposed previously on the qubit-reservoir coupling with respect to the disentanglement dynamics. Second, generation of the entanglement as well as destruction are analyzed by monitoring the reduced dynamics during and after application of a $\sqrt{\mbox{iSWAP}^\dagger}$ gate, also focusing on memory effects caused by reservoirs. Finally, the performance of a Hadamard + CNOT sequence is analyzed for different gate decomposition schemes. In all three cases, various types of noise sources and qubit parameters are considered.

Efficient characterization of noise during quantum gate operations is an essential step to building and scaling up a quantum computer. One such protocol is averaged circuit eigenvalue sampling (ACES) which efficiently characterizes a noisy gate set by reconstructing a Pauli noise model for a each gate. Here we utilize the ACES protocol to characterize two coupled superconducting qubits. For accurate reconstruction, we tailor the noise via Pauli twirling and account for measurement errors. We verify the accuracy of the protocol by comparing the predicted gate fidelities to that extracted from conventional benchmarking approaches, such as interleaved randomized benchmarking. Furthermore, we demonstrate the efficacy of ACES in accurately identifying specific noise sources by reconstructing injected phase errors in the two-qubit gates.

We investigate quantum reservoir computing (QRC) using a hybrid qubit-boson system described by the Jaynes-Cummings (JC) Hamiltonian and its dispersive limit (DJC). These models provide high-dimensional Hilbert spaces and intrinsic nonlinear dynamics, making them powerful substrates for temporal information processing. We systematically benchmark both reservoirs through linear and nonlinear memory tasks, demonstrating that they exhibit an unusual superior nonlinear over linear memory capacity. We further test their predictive performance on the Mackey-Glass time series, a widely used benchmark for chaotic dynamics, and show comparable forecasting ability. We also investigate how memory and prediction accuracy vary with reservoir parameters, and show the role of higher-order bosonic observables and time multiplexing in enhancing expressivity, even in minimal spin-boson configurations. Our results establish JC- and DJC-based reservoirs as versatile platforms for time-series processing and as elementary units that overcome the setting of equivalent qubit pairs and offer pathways toward tunable, high-performance quantum machine learning architectures.

We study the problem of optimally generating quantum gates in a logical subspace embedded in a larger Hilbert space, where the dynamics is also affected by unknown static imperfections. This general problem is widespread across various emergent quantum technology architectures. We derive the fidelity susceptibility in the computational subspace as a measure of robustness to perturbations, and define a cost function that quantifies leakage out of the subspace. We tackle both effects using a two-stage optimization where two cost functions are minimized in series. Specifically, we apply this framework to the generation of single-qubit gates in a superconducting transmon system, and find high-fidelity solutions robust to detuning and amplitude errors across various parameter regimes. We also show control pulses which maximize fidelity while minimizing leakage at all times during the evolution. However, finding control solutions that address both effects simultaneously is shown to be much more challenging, indicating the presence of a trade-off relation.

Meaningful quantum computing is currently bottlenecked by the error rates of current generation Noisy Intermediate Scale Quantum (NISQ) devices. To improve the fidelity of the quantum logic gates, it is essential to recognize the contributions of various sources of errors, including background noise. In this work, we investigate the effects of noise when applied to superconducting qubit control pulses to observe the dependency of the gate fidelity with the signal-to-noise ratio (SNR). We propose a model on how the noise of the control electronics interacts with the qubit system and demonstrate a method for characterizing the noise environment of the qubit control.

To achieve a fault-tolerant quantum computer, it is crucial to increase the coherence time of quantum bits. In this work, we theoretically investigate a system consisting of a series of superconducting qubits that alternate between XX and YY ultrastrong interactions. By considering the two-lowest energy eigenstates of this system as a {\it logical} qubit, we demonstrate that its coherence is significantly enhanced: both its pure dephasing and relaxation times are extended beyond those of individual {\it physical} qubits. Specifically, we show that by increasing either the interaction strength or the number of physical qubits in the chain, the logical qubit's pure dephasing rate is suppressed to zero, and its relaxation rate is reduced to half the relaxation rate of a single physical qubit. Single qubit and two-qubit gates can be performed with a high fidelity.

We introduce a data-driven approach for extracting two-level system (TLS) parameters-frequency $ω_{TLS}$, coupling strength $g$, dissipation time $T_{TLS, 1}$, and the pure dephasing time $T^φ_{TLS, 2}$, labelled as a 4-component vector $\vec{q}$, directly from simulated spectroscopy data generated for a single TLS by a form of two-tone spectroscopy. Specifically, we demonstrate that a custom convolutional neural network model(CNN) can simultaneously predict $ω_{TLS}$, $g$, $T_{TLS, 1}$ and $T^φ_{TLS, 2}$ from the spectroscopy data presented in the form of images. Our results show that the model achieves superior performance to perturbation theory methods in successfully extracting the TLS parameters. Although the model, initially trained on noise-free data, exhibits a decline in accuracy when evaluated on noisy images, retraining it on a noisy dataset leads to a substantial performance improvement, achieving results comparable to those obtained under noise-free conditions. Furthermore, the model exhibits higher predictive accuracy for parameters $ω_{TLS}$ and $g$ in comparison to $T_{TLS, 1}$ and $T^φ_{TLS, 2}$.

The extended Jaynes-Cummings model (eJCM) is a foundational framework for describing multi-mode light-matter interactions, with direct applications in quantum technologies such as photon addition and quasi-noiseless amplification. However, the model's complexity makes classical simulation intractable for large systems that could be of practical interest. In this work, we present a comprehensive, end-to-end framework for the quantum simulation of the eJCM. We develop explicit quantum algorithms and circuits for simulating the system's time evolution using first and second-order product formulas, analyzing the dynamics in both the Schrodinger and interaction pictures. Our analysis includes rigorous, closed-form error bounds that guide the choice of simulation parameters, and we extend the methodology to efficiently handle both pure and mixed quantum states. Furthermore, we validate our theoretical cost models with numerical simulations and provide a detailed fault-tolerant resource analysis, compiling the simulation circuits for a surface-code architecture to yield concrete estimates for physical qubit counts and execution times. This work establishes a complete roadmap for simulating the eJCM on future quantum computers.

Materials engineering using atomistic modeling is an essential tool for the development of qubits and quantum sensors. Traditional density-functional theory (DFT) does however not adequately capture the complete physics involved, including key aspects and dynamics of superconductivity, surface states, etc. There are also significant challenges regarding the system sizes that can be simulated, not least for thermal properties which are key in quantum-computing applications. The QuantumATK tool combines DFT, based on LCAO basis sets, with non-equilibrium Green's functions, to compute the characteristics of interfaces between superconductors and insulators, as well as the surface states of topological insulators. Additionally, the software leverages machine-learned force-fields to simulate thermal properties and to generate realistic amorphous geometries in large-scale systems. Finally, the description of superconducting qubits and sensors as two-level systems modeled with a double-well potential requires many-body physics, and this paper demonstrates how electron-electron interaction can be added to the single-particle energy levels from an atomistic tight-binding model to describe a realistic double-quantum dot system.

Fast, high-fidelity, and low back-action readout plays a crucial role in the advancement of quantum error correction (QEC). Here, we demonstrate high-performance multiplexed readout of superconducting qubits using a tunable broadband Purcell filter, effectively resolving the fundamental trade-off between measurement speed and photon-noise-induced dephasing. By dynamically tuning the filter parameters, we suppress photon-noise-induced dephasing by a factor of 7 in idle status, while enabling rapid, high-fidelity readout in measurement status. We achieve 99.6\% single-shot readout fidelity with 100~ns readout pulse, limited primarily by relaxation errors during readout. Using a multilevel readout protocol, we further attain 99.9\% fidelity in 50~ns. Simultaneous readout of three qubits using 100~ns pulses achieves an average fidelity of 99.5\% with low crosstalk. Additionally, the readout exhibits high quantum-nondemolition (QND) performance: 99.4\% fidelity over repeated measurements and a low leakage rate below 0.1\%. Building on the tunable broadband filter, we further propose a scalable readout scheme for surface code QEC with enhanced multiplexing capability, offering a promising solution for fast and scalable QEC.

A pivotal task for quantum computing is to speed up solving problems that are both classically intractable and practically valuable. Among these, combinatorial optimization problems have attracted tremendous attention due to their broad applicability and natural fitness to Ising Hamiltonians. Here we propose a quantum sampling strategy, based on which we design an algorithm for accelerating solving the ground states of Ising model, a class of NP-hard problems in combinatorial optimization. The algorithm employs a hybrid quantum-classical workflow, with a shallow-circuit quantum sampling subroutine dedicated to navigating the energy landscape. Using up to 104 superconducting qubits, we demonstrate that this algorithm outputs favorable solutions against even a highly-optimized classical simulated annealing (SA) algorithm. Furthermore, we illustrate the path toward quantum speedup based on the time-to-solution metric against SA running on a single-core CPU with just 100 qubits. Our results indicate a promising alternative to classical heuristics for combinatorial optimization, a paradigm where quantum advantage might become possible on near-term superconducting quantum processors with thousands of qubits and without the assistance of error correction.

The photon blockade phenomenon, a promising tool for realizing efficient single-photon sources, is the central focus of our work. We study this phenomenon within the context of the multimode extension of the Jaynes-Cummings model, incorporating two-photon dissipation and external coherent driving. Operating in the weak-driving regime, we confine our analysis to the two-excitation sector of the Hilbert space, initially exploring the single-mode case and then focusing on the corresponding multimode problem. Our study calculates the second-order correlation function (both numerically and analytically) for zero- and nonzero time delays in single- and multimode cases, to pinpoint and validate the conditions that lead to conventional and unconventional photon blockade. Our zero delay findings reveal that photon antibunching is comparable in both cases; however, the multimode case offers a greater degree of control and applicability. Furthermore, for non-zero delay operation, we find that when one of the multiple modes is set at the optimal conventional photon blockade conditions, the behavior of the curve mimics the single-mode problem with an overall slower rate of reaching the $g^{(2)}(τ)=1$ value. These results highlight the practical implications of our findings for building useful single-photon sources.

We propose a scalable fluxonium-transmon-fluxonium (FTF) system that utilizes a central transmon to mediate high-fidelity gates and parity checks between two fluxonium qubits without the need for strong non-local interactions. This approach suppresses unwanted long-range interactions, which is critical for developing larger quantum processors. First, we analyze the performance of cross-resonance (CR) CNOT gates between a fluxonium and a transmon. We show that even in the presence of a spectator qubit, these gates maintain high fidelity with coherent errors on the order of $10^{-5}$. We then demonstrate that these gates, when applied sequentially, enable high-fidelity parity checks and logical fluxonium-fluxonium CNOT gates. In addition, the central transmon can facilitate the readout of the neighboring fluxoniums, consolidating multiple critical functions into a single ancilla. Our work establishes the viability of a dual-species architecture as a promising path toward fault-tolerant quantum computation.