The integration of superconducting nanowire single-photon detectors (SNSPDs) for state readout in trapped-ion quantum processors is hindered by their sensitivity to electromagnetic interference from ion-trapping radio-frequency (rf) fields. We demonstrate a comprehensive mitigation strategy for niobium nitride ($\mathrm{Nb}\mathrm{N}$) SNSPDs that combines a conducting, optically transparent tin-doped indium oxide (ITO) shield with a nanowire of optimized geometry. Our results show that a 300-nm-wide nanowire exhibits greater resilience to rf fields compared to a 100-nm-wide device, sustaining a higher relative critical current at increased rf amplitudes. We also implement a phase-coherent active rf cancellation technique that recovers 92% of the detector’s unperturbed critical current under a $2\pi \times 25\mathrm{MHz}$ rf field. Based on these findings, we demonstrate the possibility of engineering SNSPDs for compatibility with the rf environments of surface electrode traps. This combined approach provides a robust strategy for integrating SNSPDs into trapped-ion systems, which is a critical step towards scalable quantum information processing.

Continuous-variable quantum key distribution (CV-QKD) is highly attractive for secure optical communication due to its compatibility with commercial infrastructure. In local local oscillator systems, time-division multiplexing is widely employed to compensate for phase drift caused by independent lasers. However, conventional approaches rely on a two-step compensation process that sacrifices a portion of the raw key data, thereby directly reducing the final secret key rate. To address this, we propose a single-step phase-compensation scheme using a self-supervised recurrent neural network based on the Noise2Void framework. By leveraging the temporal correlation of phase drift against the independence of measurement noise, our “blind-spot” network effectively denoises pilot phase observations without requiring clean ground truth labels. Experimental validation over an 80-km fiber link demonstrates that the proposed method reduces excess noise to 0.0035 shot-noise unit (SNU), significantly lower than the 0.0183 SNU for the traditional two-step scheme. By achieving “zero raw key overhead,” it boosts the secret key rate to 225 kbps under the asymptotic regime and 75 kbps under the finite-size regime, corresponding to increases of 46% and 140% compared with the conventional scheme. This work provides a robust, cost-effective solution for enhancing the performance of practical CV-QKD systems.

A robust definition of quantum run time is essential for assessing the performance of quantum algorithms and claims of quantum advantage. While for most classical hardware the total run time is well approximated by computation plus a weakly varying constant, on current quantum hardware a clean experimental separation between “pure computation” and “overhead” is often not justified. Consequently, conventional quantum run-time analyses that exclude substantial system-level overheads (e.g., readout, transpilation, thermalization) can lead to biased performance assessments. In this work we introduce experimentally grounded, end-to-end definitions of quantum run-time for both digital and analog quantum computers, together with a methodology for selecting strong classical baselines for quantum-classical run-time comparisons. Within this framework, we evaluate recent claims of quantum advantage in annealing- and gate-based algorithms. We examine three representative case studies. First, we revisit quantum annealing for approximate quadratic unconstrained binary optimization problems [Phys. Rev. Lett. 134, 160601 (2025)], which employs a well-motivated time-to-epsilon metric but effectively uses annealing time as a proxy for run time. Second, we analyze a restricted implementation of Simon’s problem [Phys. Rev. X 15, 021082 (2025)], where the favorable scaling in oracle calls is undisputed; however, we show that the estimated wall-clock run time of the quantum demonstration is approximately two orders of magnitude slower than a tuned classical baseline at the tested sizes. Finally, we find that the recently reported run-time advantage of the bias-field digitized counterdiabatic quantum optimization hybrid algorithm (arXiv:2505.08663) is not observed under more comprehensive benchmarking. Therefore, on current NISQ hardware, run-time-based quantum advantage has not yet been demonstrated under experimentally grounded performance metrics, and credible claims require careful time accounting, appropriate performance measures, and properly chosen classical reference implementations, as discussed in this work.

Memory, understood as time nonlocality, is a fundamental nonequilibrium feature that can arise in physical systems, whether classical or quantum, and it has important applications in a wide variety of technologies. In the context of quantum technologies, systems with memory can be used in quantum information, communication, and sensing. Here, we demonstrate that a cavity optomechanical system driven by a pulsed laser can operate as a programmable quantum memory element. By engineering the adiabatic and nonadiabatic pulses, particularly the Gaussian and sinusoidal drives, we induce and control diverse memory phenomena such as dynamical hysteresis, quantized phononic transitions, and distinct energy-storing responses. Within a mean-field approach, we derive the analytical and numerical criteria under which the photonic and phononic observables manifest memory effects in strongly driven regimes. These memory effects are quantified through a dimensionless geometric form factor, which provides a versatile metric to characterize the memory efficiency. Our protocol is readily compatible with current optomechanical platforms, highlighting its potential for advanced memory functionalities in quantum technologies.

The negatively charged tin-vacancy center in diamond (${\mathrm{Sn}\mathrm{V}}^{-}$) is an excellent solid-state qubit with optically addressable transitions and a long electron spin-coherence time at elevated temperatures (approximately 1.7 K). However, implementing scalable quantum nodes with high-fidelity optical readout of the electron spin state requires efficient photon emission and collection from the system. In this manuscript, we report a quantum photonic interface for ${\mathrm{Sn}\mathrm{V}}^{-}$ centers based on one-dimensional photonic crystal cavities fabricated in diamond thin films. Furthermore, we provide a rigorous description of the spontaneous emission dynamics of our system, taking into account individual contributions from both the C and D transitions of the emitter. This allows for the determination of Purcell factors per transition and, by extension, the C/D branching ratio ${\mathrm{Sn}\mathrm{V}}^{-}$ zero-phonon line. We observe quality factors of up to approximately 6000 across this sample, and we measure up to a 12-fold lifetime reduction, which translates into a Purcell factor of $F_{\mathrm{C}}=26.2\pm 1.5$ for a targeted C transition. By considering the cavity-mode polarization alignment with the C- and D-transition dipole moments, we validate the C/D branching ratio to be ${\eta }_{\mathrm{BR}}=0.75\pm 0.01$, in line with previous theoretical and experimental findings.

The fluxonium qubit has emerged as a promising candidate for superconducting quantum computing due to its long coherence times and high-fidelity gates. Nonetheless, further scaling up and improving performance remain critical challenges for establishing fluxoniums as a viable alternative to transmons. A key obstacle lies in developing scalable coupling architectures. In this work, we introduce a scalable fluxonium architecture that enables decoupling of qubit states while maintaining tunable couplings between noncomputational states. Beyond the well-studied $ZZ$ crosstalk, we identify that always-on interactions involving noncomputational levels can significantly degrade the gate fidelities in large systems, thereby impeding scalability. Based on two possible physical realizations of the architecture, we demonstrate that the issue can be mitigated by implementing tunable couplings for fluxonium plasmon transitions, meanwhile enabling fast, high-fidelity gates with passive $ZZ$ suppression. This comparative analysis enables us to establish general principles for realizing the architecture while understanding and addressing implementation-specific challenges.

The measurement-based architecture is a paradigm of quantum computing, relying on the entanglement of a cluster of qubits and the measurements of a subset of it, conditioning the state of the unmeasured output qubits. While methods to map the gate model circuits into the measurement-based are already available via intermediate steps, we introduce a paradigm for quantum compiling directly converting any quantum circuit to a class of graph states, independently from its size. This method relies on the stabilizer formalism to describe the register of the input qubits. An equivalence class between graph states able to implement the same circuit is defined, giving rise to a gauge freedom when compiling in the MBQC frame. The graph state can be rebuilt from the circuit and the input by employing a set of graphical rules. A system of equations describes the overall process. Compared with measurement calculus, the ancillary qubits are reduced by 50% on the quantum Fourier transform and 75% on the Quantum Approximate Optimization Algorithm, still preserving similar scaling laws for the number of entangling gates.

As a branch of quantum machine learning, quantum reinforcement learning (QRL) aims to solve complex sequential decision-making problems more efficiently and effectively than its classical counterpart by exploiting quantum resources. However, in the noisy intermediate-scale quantum (NISQ) era, its realization is challenged by the ubiquitous noise-induced decoherence. Here, we propose a noise-resilient QRL scheme for a quantum eigensolver with a two-level system as an agent. By investigating the non-Markovian decoherence effect on the QRL for solving the eigenstates of the agent-environment interaction Hamiltonian, we find that the formation of a bound state in the energy spectrum of the total agent-noise system restores the QRL performance to that in the noiseless case. Providing a universal physical mechanism to suppress the decoherence effect on quantum machine learning, our result lays the foundation for designing NISQ algorithms and offers a guideline for their practical implementation.

Quantum machine learning (QML) has the potential to achieve quantum advantage for specific tasks by combining quantum computation with classical machine learning (ML). In classical ML, a significant challenge is membership-privacy leakage, whereby an attacker can infer from model outputs whether specific data were used in training. When specific data are required to be withdrawn, removing their influence from the trained model becomes necessary. Machine unlearning (MU) addresses this issue by enabling the model to forget the withdrawn data, thereby preventing membership-privacy leakage. However, this leakage remains underexplored in QML. This raises two research questions: do QML models leak membership privacy about their training data, and can MU methods efficiently mitigate such leakage in QML models? We investigate these questions using two quantum neural network (QNN) architectures, a basic QNN and a hybrid QNN, evaluated in noiseless simulations and cloud quantum device demonstrations. To answer the first question, we analyze how quantum constraints shape membership-privacy leakage in QML and then formalize a realistic gray-box threat model accordingly. Based on this, we design a membership inference attack (MIA) tailored to QNN outputs, and our results provide clear evidence of membership leakage in both QNNs. To answer the second question, we propose a quantum machine unlearning (QMU) framework, comprising three MU mechanisms. Evaluations on two QNN architectures show that QMU removes the influence of the withdrawn data while preserving accuracy for retained data. A comparative analysis further characterizes the three MU mechanisms with respect to data dependence, computational cost, and robustness. We further study how the shot count in quantum measurement affects both membership leakage and unlearning stability. Overall, this work provides a potential path toward privacy-preserving QML.

We propose a practical, physics-inspired benchmarking suite to challenge both quantum and classical computers by mapping real-time quantum dynamics to a common optimization format. Using a parallel-in-time encoding, we convert the real-time propagator of an $n$-qubit, possibly non-Hermitian, Hamiltonian into quadratic unconstrained binary optimization (QUBO) instances that are executable in a solver-agnostic manner on quantum annealers and classical optimizers alike. This enables direct, like-for-like performance comparisons across fundamentally different computational paradigms. To stress-test the framework, we consider eight representative dynamical models spanning single-qubit rotations, multiqubit entangling gates (Bell, Greenberger-Horne-Zeilinger, cluster), and $\mathcal{P}\mathcal{T}$-symmetric and other non-Hermitian generators, and evaluate success probability and time to solution as standard benchmarking metrics. Applying this method to two generations of D-Wave quantum annealers and to state-of-the-art classical solvers (simulated annealing and the graphics-processing unit–accelerated VeloxQ), we find that Advantage2 consistently outperforms its predecessor, while VeloxQ retains the shortest absolute run-times, reflecting the maturity of classical heuristics. We further extend the benchmarks to large-scale instances ($N\approx {10}^5$), establishing a demanding classical baseline for future hardware. Together, these results position the parallel-in-time QUBO framework as a versatile and physically motivated test bed for quantitatively tracking progress toward quantum-competitive simulation of dynamical systems.

While geometric quantum gates are often theorized to possess intrinsic resilience to control errors by exploiting the global properties of evolution paths, this promise has not consistently translated into practical robustness. We present a streamlined framework for nonadiabatic geometric quantum gates (NGQGs) that incorporates additional auxiliary constraints to suppress dynamical contamination and achieve super-robust performance. Within this framework, we also design NGQGs using open paths, offering enhanced design flexibility. Implemented on superconducting transmon qubits, our scheme realizes high-fidelity single-qubit gates that are robust against Rabi amplitude error $ϵ$, with infidelity scaling as $\mathcal{O}({ϵ}^4)$, in contrast to the $\mathcal{O}({ϵ}^2)$ behavior of conventional dynamical gates. We further analyze two-qubit NGQGs under parametric driving. Our results identify subtle limitations that compromise performance in two-qubit scenarios, underscoring the significance of phase compensation and waveform calibration. The demonstrated simplicity and generality of our super-robust NGQG scheme make it applicable across diverse quantum platforms.

Decoherence in superconducting qubits is dominated by defects that reside at amorphous interfaces. Interaction with discrete defects results in dropouts that complicate qubit operation and lead to non-Gaussian tails in the distribution of qubit energy relaxation time $T_1$ that degrade system performance. Spectral diffusion of defects over time leads to fluctuations in $T_1$, posing a challenge for calibration. In this work, we measure the energy relaxation of flux-tunable transmons over a range of operating frequencies. We vary qubit geometry to change the interface participation ratio by more than an order of magnitude. Our results are consistent with loss dominated by discrete interfacial defects. Moreover, we are able to localize the dominant defects to within 500 nm of the qubit junctions, where residues from liftoff are present. These results motivate alternative approaches to qubit junction fabrication that avoid the residues intrinsic to the liftoff process.

Quantum computers require high-fidelity measurement of many qubits to achieve a quantum advantage. Traditional approaches suffer from readout crosstalk for a neutral-atom quantum processor with a tightly spaced array. Although classical machine learning algorithms based on convolutional neural networks can increase fidelity, they are computationally expensive, making it difficult to scale them to large qubit counts. We present two simpler and scalable machine learning algorithms that realize matched filters for the readout problem. One is a local model that focuses on a single qubit, and the other uses information from neighboring qubits in the array to prevent crosstalk among the qubits. We demonstrate error reductions of up to 32% and 43% for the site and array models, respectively, compared with a conventional Gaussian threshold approach. Additionally, our array model uses 2 orders of magnitude fewer trainable parameters and 4 orders of magnitude fewer multiplications and nonlinear function evaluations than a recent convolutional neural network approach, with only a minor (3.5%) increase in error across different readout times. Inference is more than 350 times faster for our local model and more than 540 times faster for our array model compared with the respective convolutional neural networks. Another strength of our approach is its physical interpretability: the learned filter can be visualized to provide insights into experimental imperfections. We also show that two existing convolutional neural network models can be pruned to have 70 times and 4000 times fewer parameters, respectively, while maintaining similar errors. Our work shows that simple machine learning approaches can achieve high-fidelity qubit measurements while remaining scalable to systems with larger qubit counts.

dc voltage–biased Josephson junctions have recently been used in superconducting circuits for Hamiltonian engineering, demonstrating microwave amplification, single-photon sources, and entangled photon generation. Compared with more conventional approaches based on parametric pumps, this solution typically enables larger interaction strengths. In the context of quantum information, a two-to-one photon interaction can stabilize cat qubits, where bit-flip errors are exponentially suppressed, promising significant resource savings for quantum error correction. This work investigates how the dc bias approach to Hamiltonian engineering can benefit cat qubits. We find a simple circuit design that is predicted to showcase a two-to-one photon exchange rate larger than that of the parametric pump–based implementation while dynamically averaging typically resonant parasitic terms such as Kerr terms and cross-Kerr-terms. In addition to addressing qubit stabilization, we propose using injection locking with a cat qubit–adapted frequency filter to prevent long-term drifts of the cat qubit angle associated with dc voltage noise. The whole scheme is simulated without rotating-wave approximations, highlighting the amplitude of related oscillatory effects in cat-qubit-stabilization schemes. This study lays the groundwork for the experimental realization of such a circuit.

The realization of multiqubit entangling gates is essential for efficient, scalable, and fault-tolerant quantum information processing, reducing algorithmic complexity and circuit depth. We demonstrate a native three-qubit entangling gate implemented by simultaneously driving all qubits at a common frequency, exploiting engineered interactions to realize multicontrol operations in a single coherent step. By optimizing the conditional dynamics originating from drive-induced nonlocal contamination, desired interaction channels are selectively enhanced while spurious terms are suppressed, ensuring robust performance within the computational subspace. This gate enables key applications, including deterministic GHZ-state generation, Toffoli-class logic with a shortest gate duration of 90 ns and a highest fidelity of 99.72%, and a controlled-$ZZ$ gate tailored for fast surface-code quantum error correction. Simulations based on realistic IBM device parameters indicate that the gate maintains high fidelity and resilience under increasing excitation numbers and larger Hilbert-space dimensions. Our results establish a foundation for co-designing circuit architectures and control strategies that harness native multiqubit interactions as fundamental building blocks for next-generation superconducting quantum processors, thereby enabling improved gate performance with more flexible tuning of circuit parameters.

We present a probabilistic quantum algorithm for preparing mixed states which, in expectation, are proportional to the solutions of Lyapunov equations—linear matrix equations ubiquitous in the analysis of classical and quantum dynamical systems. Building on previous results by Zhang et al., arXiv:2304.04526, at each step the algorithm can (i) return the current state, (ii) apply a trace nonincreasing completely positive map, or (iii) restart. We introduce a deterministic stopping rule, which leads to an efficient algorithm with a bounded expected number of calls to oracles representing the two input matrices of the Lyapunov equations. We also consider preparing a mixed state that approximates the normalized inverse of a positive definite matrix $A$. In its most general form, the algorithm generates mixed states, which approximate matrix-valued weighted sums and integrals. It can be shown that block encodings and states yield two incomparable computational resources even when they represent the same piece of data. While block encodings of functions have received much attention in the literature, our work takes a step toward the less explored problem of encoding functions into mixed states.

Quantum linear system (QLS) algorithms offer the potential to solve large-scale linear systems exponentially faster than classical methods. However, applying QLS algorithms to real-world problems remains challenging due to issues such as state preparation, data loading, and efficient information extraction. In this work, we study the feasibility of applying QLS algorithms to solve discretized three-dimensional (3D) heterogeneous Poisson equations, with specific examples relating to groundwater flow through geologic fracture networks. We explicitly construct a block encoding for the 3D heterogeneous Poisson matrix by leveraging the sparse local structure of the discretized operator. While classical solvers benefit from preconditioning, we show that block encoding the system matrix and preconditioner separately does not improve the effective condition number that dominates the QLS run-time. This differs from classical approaches where the preconditioner and the system matrix can often be implemented independently. Nevertheless, due to the structure of the problem in three dimensions, the quantum algorithm achieves a run-time of $O(N^{2/3}\text{polylog }N\cdot \log (1/ϵ))$, outperforming the best classical methods (with run times of $O(N\log N\cdot \log (1/ϵ))$) and offering exponential memory savings. These results highlight both the promise and limitations of QLS algorithms for practical scientific computing, and point to effective condition-number reduction as a key barrier in achieving quantum advantages.

We describe a general quantum receiver protocol that maps laser-light-modulated classical communications signals into quantum processors for decoding with quantum logic gates. The quantum gates enable joint quantum measurements over a code word to achieve the quantum limit of communication capacity. Our receiver design requires qubit resources that only logarithmically increase with the size of the code word and accommodates practically relevant coherent-state modulation containing multiple photons per pulse. Focusing on classical-quantum polar codes, we outline the necessary quality of quantum operations and code-word lengths to demonstrate a quantum-processing-enhanced communication rate surpassing that of any known classical optical receiver-decoder pair. Specifically, we show that a small quantum receiver of four qubits with operational errors of approximately $0.2\mathrm{\%}$ can already provide a 5% gain in the communication rate in the weak-signal limit. Additionally, we outline a possible hardware implementation of the receiver where efficient spin-photon interfaces such as cavity-coupled diamond color centers or atomic qubits are used to input the received photonic signal to a small-scale quantum processor for decoding. Our results outline a promising route for potential quantum advantage in classical communication with near-term, small-scale quantum computers.