A limiting factor to the exploitation of continuous variables in quantum computing, in particular in the field of quantum machine learning, has been the hindrances of computing the derivatives of a model. The constraint occurs when applying the parameter-shift rule to a photonic circuit, and the number of non-Gaussian gates exceeds a logarithmic trend with respect to the length of the circuit. This condition occurs when dealing with most nonlinear functions. Indeed, it hampers subroutines, such as backpropagation, which are paramount for machine learning and, most significantly, the computation of the derivatives of the model itself. To overcome this hurdle, we propose a method to compute derivatives of functions, implemented by continuous variable circuits, directly through photonic quantum circuits. Unlike the parameter-shift rule, this method does not fail when the number of non-Gaussian gates scales more than logarithmically against the depth of the circuit. We exploit the fact that computing the derivative of a wave function is equivalent to applying the momentum operator, which in the position basis acts as its derivative. The action of this operator is physically embodied by photon-added coherent states. We prove that, up to the second order of derivation, the number of operations does not scale with the depth of the circuit, but only with the order of derivation. We showcase numerical examples of derivatives of elementary functions parametrized on simulated photonic quantum circuits. Thanks to this method, we unleash an embodiment of quantum neural networks on photonic quantum computing platforms.

The ability to reliably inscribe information on single photons is a crucial requirement for quantum applications such as quantum communication and measurement-based photonic quantum computation. Experimental implementations generally employ phase modulators in single-pass or Mach-Zehnder-interferometer configurations to encode information on photonic qubits. However, these approaches are intrinsically sensitive to environmental influences, which limits the achievable quantum error rates in practice, posing a major bottleneck in the transition of quantum applications with deterministic sources from the laboratory to the field. Inspired by the adaptation of Sagnac interferometer encoders for laser-based applications, we report a demonstration of a polarization encoder for single-photon qubits based on a free-space Sagnac interferometer. The device showcases inherent phase stability and overcomes previous error-rate limitations: Telecom-wavelength photons emitted by a quantum dot are modulated at a rate of 152 MHz and with a quantum bit error rate of $0.69\pm 0.02\%$, marking the lowest value reported to date for MHz-rate information encoding on single photons by a factor of $>3$. This combination of low error rate, high modulation speed, stability, and operation with telecom-wavelength sub-Poissonian photons is an essential requirement for bringing quantum applications based on deterministic photon sources from the laboratory to the field and a significant step toward unlocking the full potential of a future quantum Internet.

High-rate remote entanglement between photon and matter-based qubits is essential for distributed quantum information processing. A key technique to increase the modest entangling rates of existing long-distance quantum networking approaches is multiplexing. Here, we demonstrate a temporally multiplexed ion-photon interface via rapid transport of a chain of nine calcium ions across $74\mathrm{\mu }\mathrm{m}$ within $86\mathrm{\mu }\mathrm{s}$. The nonclassical nature of the multiplexed photons is verified by measuring the second-order correlation function with an average value of $g^{(2)}(0)=0.060(13)$. This indicates low crosstalk of about 1% between the multiplexed modes, which can be reduced further once coupling of the photons to a single-mode fiber is incorporated. In addition, we characterize the motional degree-of-freedom of the ion crystal after transport and find that it is coherently excited to ${\overline{n}}_{\alpha }\approx 110$ for the center-of-mass mode. Our proof-of-principle implementation paves the way for large-scale quantum networking with trapped ions, but highlights some challenges that must be overcome.

Satellite-based quantum key distribution (QKD) offers a promising approach toward the realization of global-scale quantum communication networks. However, current systems are restricted to nighttime operation, as intense daytime background noise drastically degrades the signal-to-noise ratio. While single-mode fiber coupling effectively suppresses background noise, maintaining high coupling efficiency and stability amidst strong atmospheric turbulence remains a significant challenge. In this study, we propose a deep-learning-based adaptive-optics method for turbulence correction, which is experimentally validated over 1.4- and 7-km free-space channels and compared with wave-front-sensor-based systems. The proposed method achieves substantial improvements in the Strehl ratio under strong turbulence conditions. Furthermore, we numerically evaluate the performance of a free-space quantum key distribution system incorporating our deep-learning-based method, which achieved higher key rates and longer propagation distances. By directly estimating phase information from a defocused image, this approach provides an efficient and cost-effective solution for strong turbulence correction, paving the way for practical daytime QKD.

Quantum architecture search (QAS) has shown significant potential for automating quantum circuit design in variational quantum algorithms (VQAs). However, the application of current QAS methods to large-scale circuits remains challenging due to the high computational complexity of evaluating numerous circuit architectures. While performance predictors are a promising solution, they are conventionally trained with objectives such as mean squared error, which create a fundamental objective mismatch by failing to prioritize the discovery of the most elite, high-performing architectures. In this work, we resolve this objective mismatch by introducing a new training paradigm that directly optimizes a top-tier-focused ranking loss. To capture the causal information flow of quantum gates, we design a circuit-native Transformer equipped with reachability-based unitary-flow attention and depth-aware positional encoding. Furthermore, we implement a pretraining strategy based on circuit learnability, a metric that unifies expressibility and trainability, to extract functionally aware representations from unlabeled data, thereby significantly improving sample efficiency. Extensive results on multiple VQA benchmarks demonstrate that our approach consistently outperforms existing QAS frameworks in both ranking quality and the performance of the discovered circuits. Ablation studies confirm that the synergy of the differentiable top-tier-focused loss, the circuit-native Transformer, and learnability-based pretraining is essential for superior performance. Beyond energy-based benchmarks, we further validate the searched circuits under a device-calibrated noise model based on the IBM Kingston quantum computer and on a noisy 2D transverse-field Ising model benchmark. The results show that our method can identify compact circuits with fewer two-qubit gates while preserving physically meaningful properties, including fidelity, connected spin correlations, and magnetization.

We introduce excited local quantum annealing (ExcLQA), a classical, physics-inspired algorithm that extends local quantum annealing (LQA) to identify excited states of classical Ising Hamiltonians. LQA simulates quantum annealing while constraining the quantum state to remain in a product state and uses a gradient-based approach to find approximate solutions to large-scale quadratic unconstrained binary optimization problems. ExcLQA extends this framework by adding a penalty term in the cost function to target excited states, with a single hyperparameter that can be tuned via binary search to set the desired penalization level. We benchmark ExcLQA on fully connected Ising models with random interactions and on the shortest vector problem (SVP). The latter is a fundamental lattice problem underlying the security of many postquantum cryptographic schemes, and its solution can be mapped to the first excited state of an Ising Hamiltonian. For the fully connected Ising models, we show that, on the tested instances, ExcLQA outperforms both a matrix-product-state-based method and simulated annealing. Notably, even when only a lower bound on the ground-state energy is provided, rather than the exact ground-state information required by these competing methods, ExcLQA still achieves superior performance. For the SVP, ExcLQA finds exact solutions for instances up to rank 46 and outperforms the Metropolis-Hastings algorithm in terms of solved ratio, number of shots, and approximation factor on the tested instances.

We develop a theory for two quasiparticle-induced decoherence mechanisms of a driven superconducting qubit. In the first mechanism, an existing quasiparticle (QP) tunnels across the qubit’s Josephson junction while simultaneously absorbing a qubit excitation and one (or several) photons from the drive. In the second mechanism, a qubit transition occurs during the nonlinear absorption process converting multiple drive quanta into a pair of new QPs. Both mechanisms can remain significant in gap-engineered qubits whose coherence is insensitive to QPs without the drive. Our theory establishes a fundamental limitation on fidelity of the microwave qubit operations—such as readout and gates—stemming from QPs.

Isolators are commonly found in the amplification chain of microwave setups to shield sensitive devices such as superconducting qubits from noise and back-scattered signals. Conventional ferrite-based isolators are bulky, lossy, and rely on strong magnetic fields, which pose challenges for their co-integration in large-scale superconducting devices. Although several magnetless approaches based on parametric modulation have been explored to overcome these limitations, none has yet experimentally demonstrated wideband isolation on a par with ferrite devices. Here, we propose a compact modulation-based isolator that achieves large isolation bandwidth using a dispersion-engineered transmission line. The engineered line forms an effective two-mode system that enables broadband isolation by supporting adiabatic mode conversion over a wide instantaneous bandwidth. Numerical simulations show that this architecture can provide more than 20-dB isolation across 4–8 GHz, matching the performance of ferrite-based isolators. Moreover, we propose an on-chip superconducting device implementation that shows promise against parameter variations and enables a scalable path for co-integration with future large-scale superconducting systems.

We propose methods to mitigate single- and two-qubit control errors due to residual exchange coupling in systems of exchange-coupled resonant singlet-triplet qubits. Commensurate driving, where the pulse length is an integer multiple of the drive period, can mitigate errors from residual intraqubit exchange, including effects from counter-rotating terms and off-axis rotations, as well as leakage errors during two-qubit operations. Residual interqubit exchange creates crosstalk errors that reduce single-qubit control fidelities. We show that using a single-spin coupler between two resonant singlet-triplet qubits can reduce this crosstalk error by an order of magnitude. Assuming perfect coupler state preparation and realistic charge and hyperfine noise, we predict that coupler-assisted two-qubit gate errors can be below $3\times {10}^{-3}$ for gate times as short as $66\mathrm{ns}$, even in the presence of residual exchange levels exceeding several hundred kHz. Our results suggest the potential of utilizing coupler-based architectures for large-scale fault-tolerant spin qubit processors based on resonant singlet-triplet qubits.

Longitudinal coupling offers a compelling pathway for quantum nondemolition (QND) readout, but pulse design is constrained by hardware limitations, such as the maximum coupling strength and the intracavity photon number required to remain in the linear regime. Here, we formulate the pulse-design task as a low-dimensional constrained optimization problem. Building on inverse engineering (IE) within the shortcuts to adiabaticity (STA) framework, we parameterize the auxiliary control variable with a smooth B-spline basis and reconstruct the physical control from it. The analytical STA pulse provides the initialization for this constrained search. At a fixed short readout time, the optimized pulse yields an approximately 50% improvement in $\mathrm{SNR}$ over the STA baseline. Comparisons across multiple optimizers show that the IE-guided formulation consistently yields high-performance solutions, while the optimized pulse remains smooth, hardware compatible, and governed by an interpretable “saturate-and-hold” mechanism.

Multimode superconducting circuits offer a promising platform for engineering robust systems for quantum computation. Previous studies indicate that single-mode devices cannot be engineered to simultaneously exhibit resilience against multiple decoherence sources due to conflicting requirements. In contrast, multimode systems offer increased flexibility and have proven capable of overcoming these fundamental limitations. Here, we present a multimode device optimized for quantum information processing. It features an anharmonicity of a third of the qubit frequency and reduced energy dispersion caused by charge and magnetic flux fluctuations. It exhibits improvements over the fundamental errors limiting transmon and fluxonium coherence and control, achieving ratios between the total coherence time and the gate time $T_2/t_{\mathrm{g}}$ one order of magnitude larger than transmon and two times larger than fluxonium for microwave charge drives, assuming equal dielectric and inductive loss quality factors and limited drive strength. Simulations further demonstrate the device’s robustness to circuit parameter deviations, a known limitation of many multimode devices.

High-fidelity quantum-state preparation is a central task in quantum information science. In practice, it is commonly guided either by full quantum state tomography, which becomes prohibitively resource-intensive as system size grows, or by empirically chosen measurement settings that lack principled optimality. Here, we show that quantum state verification (QSV) can be elevated from a purely diagnostic tool to a prescriptive framework for quantum-state preparation, directly specifying experimentally optimal measurements and quantitative fidelity indicators without full state reconstruction. We experimentally realize this prescriptive paradigm using a three-qubit nonstabilizer $W$ state and a modified homogeneous QSV protocol. The verification measurements not only certify the prepared state with high confidence but also serve as a tomography-free indicator that systematically informs the preparation procedure. Using only nine measurement settings and ${10}^4$ samples, we achieve high-fidelity state preparation consistent with full tomography that requires orders of magnitude more resources. Beyond the present implementation, the prescriptive structure of QSV is naturally compatible with closed-loop feedback control, outlining a pathway toward genuine real-time quantum-state preparation in future low-latency platforms.

The semiconductor chip manufacturing process is complex and lengthy, and potential errors can arise at every stage. Each wafer contains numerous chips, and wafer bin maps can be generated after chip testing. By analyzing the defect patterns on these wafer bin maps, the manufacturing steps at which errors occur can be inferred. In this work, we improve the quantum Bayesian inference algorithm and apply the proposed algorithm to accelerate the identification of error patterns on wafer bin maps, thereby assisting in chip yield analysis. We show the algorithm for error identification and detail the implementation of the improved quantum Bayesian inference. Our results demonstrate the speed advantage of quantum computation over classical algorithms for a real-world problem, highlighting the practical significance of quantum computation for many problems based on Bayesian inference.

We present a countermeasure against the detector-blinding attack (DBA) utilizing statistical analysis of error and double-click events accumulated during a quantum key distribution session under randomized modulation of single-photon avalanche diode (SPAD) detection probabilities via gate voltage manipulation. Building upon prior work demonstrating the ineffectiveness of this countermeasure against continuous-wave (cw) DBA, we extend the analysis to evaluate its performance against pulsed DBA. Our findings reveal an approximately 13 dB increase in the trigger pulse energies difference between default and reduced gate voltage applied under pulsed DBA conditions compared with cw DBA. This heightened difference enables a re-evaluation of the feasibility of utilizing SPAD detection probability variations as a countermeasure and makes it possible to estimate the fraction of bits compromised by an adversary during pulsed DBA.

Numerical calculus algorithms, which estimate derivatives and integrals from data series acquired either via measurements or by sampling functions, are essential in scientific computing. To date, few quantum algorithms have been developed to perform calculus operations based on closed-form functional inputs; yet, in many practical applications, field variables are numerically described via a series of samples rather than closed-form expressions. This paper presents the theoretical development and the gate-level circuit implementation of quantum algorithms for numerical differentiation and, up to addition of a constant, indefinite integration. The methodology relies on a spectral approach that leverages the computational efficiency of the quantum Fourier transform and the parallel computing capability afforded by quantum superposition to evaluate outputs at all domain points simultaneously. The differentiation approach is also extended to enable gradient estimation, and postprocessing procedures are presented to recover sign information. The primary outputs of the proposed algorithms are quantum state vectors directly proportional to the numerical derivative or integral of the given data; therefore, the correctly signed results are made available to proceeding quantum computations. This result lays the foundation for the proposed algorithms to serve as core subroutines in applied quantum computing operations, such as image processing, data analysis, and machine learning.

Zinc oxide (ZnO) has garnered much attention as a promising material for quantum devices due to its unique characteristics. To utilize the potential of ZnO for quantum devices, the development of fundamental technological elements such as high-speed readout and charge sensing capabilities has become essential. In this study, we address these challenges by demonstrating radio-frequency (rf) reflectometry and charge sensing in ZnO quantum dots, thus advancing the potential for qubit applications. A device is fabricated on a high-quality ZnO heterostructure, featuring gate-defined target and sensor quantum dots. The sensor dot, integrated into an rf resonator circuit, enables the detection of single-electron charges in the target dots. Using this setup, we observe the rf-detected charge stability diagram indicating the formation of few-electron double quantum dots and provide the first benchmark of the bandwidth, sensitivity, and readout fidelity of rf reflectometry.

We demonstrate a dissipative protocol for ground-state preparation of a quantum spin chain on a trapped-ion quantum computer. As a first step, we derive a Kraus representation of a dissipation channel for the protocol recently proposed by Ding et al. [Single-ancilla ground state preparation via lindbladians, Phys. Rev. Res. 6, 033147 (2024)] that still holds for arbitrary temporal discretization steps, extending the analysis beyond the Lindblad dynamics regime. The protocol guarantees that the fidelity with the ground state monotonically increases (or remains unchanged) under repeated applications of the channel to an arbitrary initial state, provided that the ground state is the unique steady state of the dissipation channel. Using this framework, we implement dissipative ground-state preparation of a transverse-field Ising chain for up to 19 spins on the trapped-ion quantum computer Reimei provided by Quantinuum. Despite the presence of hardware noise, the dynamics consistently converges to a low-energy state far away from the maximally mixed state even when the corresponding quantum circuits contain as many as 4110 entangling gates, demonstrating the intrinsic robustness of the protocol. By applying zero-noise extrapolation, the resulting energy expectation values are systematically improved to agree with noiseless simulations within statistical uncertainties.

In this study, we report a robust cryptographic scheme operating over noisy channels, enabled by a nonlinear detection architecture that intrinsically mitigates channel-induced phase aberrations. Difference-frequency generation (DFG) in a nonlinear crystal is employed to automatically compensate channel-induced phase aberrations, providing intrinsic noise mitigation. Spatial modes in the orbital angular momentum basis are used as high-dimensional information carriers, providing enhanced capacity and improved security. As a classical proof-of-principle, our results demonstrate that the DFG correction mechanism reliably restores the spatial mode transfer matrix to near-ideal fidelity across a broad range of aberration types and strengths, recovering projected secret key rates and mutual information metrics consistent with a secure high-dimensional channel. These findings establish nonlinear wave mixing as a viable tool for noise-resilient, high-dimensional communication, with a clear pathway toward quantum implementation using single-photon sources and detectors.

The rapid development of machine learning and quantum computing has placed quantum machine learning (QML) at the forefront of research. However, existing QML algorithms based on quantum variational algorithms face challenges in trainability and noise robustness. To address these challenges, we introduce a gradient-free, noise-robust quantum reservoir computing algorithm that harnesses discrete time crystal dynamics as a reservoir. We characterize the memory, nonlinear, and information scrambling capacities of the quantum reservoir, revealing their correlation with dynamical phases and nonequilibrium phase transitions. For ten-class classification, both noisy simulations and experimental results on superconducting quantum processors match ideal simulations, demonstrating that accuracy increases with system size and confirming topological robustness against noise. Our work presents the first experimental demonstration of quantum reservoir computing for image classification based on digital quantum simulation. Besides, it also experimentally demonstrates the relationship between many-body nonequilibrium dynamical phase transitions and QML performance. These findings introduce noise-robust and efficient design principles for quantum reservoir computing and broader quantum machine learning algorithms in the NISQ era.