We propose a step-barrier diode based on a monolayered transition metal dichalcogenide that simultaneously exhibits complete valley and spin filtering and pronounced multipeak negative differential resistance (NDR) under off-resonant circularly polarized light (CPL). The NDR originates from resonant tunneling via a quantum well formed by the step barrier and forward bias. The valley selectivity of CPL makes the current valley polarized. Owing to the strong intrinsic spin-orbit coupling, this valley polarization accompanies a spin polarization with the same amplitude. Current calculated by the scattering-matrix formalism shows that tuning the barrier height can amplify the NDR magnitude and produce more current peaks. Reversing the CPL helicity or slightly adjusting the bias voltage switches the dominant transport channel between the $K$ and $K^{\mathrm{\prime }}$ valleys and related spins. This enables electrically and optically reconfigurable valley and spin filtering and NDR within a single device. The proposed device offers an experimentally accessible route toward multivalued logic and high-speed valleytronic applications.

Magnetized plasmas support nonreciprocal surface waves, but their operating frequency is typically set by the plasma density and magnetic bias. Here, nonreciprocal surface and leaky wave propagation at a magnetized plasma interface terminated by a high-impedance surface (HIS) is investigated. The dispersive surface impedance is treated as a boundary condition that directly modifies the surface wave dispersion. An analytical model predicts impedance-induced band splitting and additional nonreciprocal branches, enabling frequency-selective control of supported modes and their propagation direction. Full-wave simulations and microwave experiments using plasma columns validate the theory and demonstrate that tuning the HIS resonance shifts and reshapes the nonreciprocal bands without changing plasma parameters. The analysis of fields confirms unidirectional propagation and distinguishes confined surface modes from radiative leaky modes. The results establish impedance engineering as a simple and passive mechanism for frequency-agile nonreciprocity and provide a practical route toward compact isolators, circulators, and reconfigurable nonreciprocal microwave devices.

For the ever-growing field of quantum information processing, large-scale, efficient multiport interferometers serving as photonic processors are required. In this context, the suitability of quantum walks as the interferometric base for universal computation has been theoretically proven. In this work, we bridge the gap between theoretical proposals and state-of-the-art experimental capabilities by providing the recipe for the implementation of a universal photonic processor in discrete-time quantum walks. Specifically, we present the protocol for translating arbitrary linear transformations into the coin and step operator of a quantum walk and map these to the experimental parameters of the established time-multiplexed platform [A. Schreiber et al., Phys. Rev. Lett. 104, 050502 (2010)]. We show that our interface is highly scalable and resource efficient due to the hybrid encoding consisting of multiple degrees of freedom. Finally, we prove that our system is highly resilient against experimental imperfections and show that it compares favorably against existing architectures.

Fast, high-fidelity, and low-backaction 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 during measurement. Using 100 ns readout pulses, we achieve low crosstalk simultaneous readout of three qubits with a relaxation-limited average fidelity of 99.5%. By using a multilevel readout technique, we overcome the relaxation constraint and achieve a fidelity of 99.9% with a 50 ns readout pulse. The system also exhibits high-performance quantum-nondemolition characteristics, maintaining 99.4% fidelity over repeated measurements with a qubit state leakage rate below 0.1%. Building on the tunable filter, we further propose a scalable readout scheme for surface code implementation with enhanced multiplexing capability, providing a promising pathway toward fast and scalable QEC.

Halbach spheres provide a theoretically elegant means of generating highly homogeneous magnetic fields, but practical implementation is hindered by challenging fabrication and restricted interior access. This study examines discrete spherical Halbach configurations assembled from permanent magnets placed at the vertices of Platonic and Archimedean solids. Analytical calculations, numerical field simulations, and experimental measurements indicate that polyhedra with icosahedral symmetry achieve the most favorable balance among field strength, homogeneity, and interior accessibility. They produce exceptionally flat fourth-order central saddle points, resulting in a usable homogeneous field volume up to a factor of 260 larger than that of traditional Halbach disk or cylindrical arrays. Several magnet assemblies composed of cubical $\mathrm{Nd}$-$\mathrm{Fe}$-$\mathrm{B}$ magnets are fabricated and their three-dimensional field distributions characterized, demonstrating homogeneous regions of up to several cubic centimeters with deviations below $1\mathrm{\%}$. The findings establish discrete icosahedrally symmetric magnet arrays as practical, scalable building blocks for compact, highly homogeneous magnetic field sources suited to mobile magnetic resonance, and magnetophoretic applications.

Platinum ($\mathrm{Pt}$) is one of the most promising spin-source materials for spin-orbit-torque (SOT) devices owing to its high spin Hall ratio and low resistivity. However, the high-temperature thermal processing required during fabrication of magnetic tunnel junctions causes $\mathrm{Pt}$ to agglomerate, degrading interface quality and thereby compromising SOT efficiency. In this work, we demonstrate that titanium ($\mathrm{Ti}$) doping simultaneously enhances the spin Hall ratio of $\mathrm{Pt}$ and preserves its performance after high-temperature annealing. Systematic spin-torque ferromagnetic resonance and spin Hall magnetoresistance measurements reveal that the optimally $\mathrm{Ti}$-doped alloy achieves 30% enhancement in spin Hall ratio while maintaining its performance after annealing even at 400 °C. In contrast, pure $\mathrm{Pt}$ experiences a 77% reduction in efficiency due to the loss of interfacial spin transparency. Structural characterization further indicates that $\mathrm{Ti}$ doping kinetically suppresses $\mathrm{Pt}$ atomic diffusion and thermodynamically stabilizes the interface, preventing agglomeration-induced roughening. These findings establish $\mathrm{Pt}\mathrm{Ti}$ alloys as thermally stable spin-source materials for energy-efficient SOT-magnetoresistive random-access memory applications.

In this work, we establish a robust protocol for designing and fabricating adaptive electrostatic thin-film phase plates, as demonstrated by the first experimental realization of such a device and its characterization. A phase plate placed before the sample in a transmission electron microscope (TEM) can shape the beam to create user-configured irradiation with predesigned amplitude and phase distribution. It is, therefore, possible to use a phase plate to compensate for aberrations of the beam, which are a common problem in electron microscopy. The electron-optical index of refraction is proportional to the electric and magnetic potentials. Thus, the active part of a phase plate is designed to generate a local electric and/or magnetic field. By using a specific set of electrodes, the imprinted phase distribution of the electron beam can be adjusted externally by changing the potential applied to the electrodes. This is denoted as an adaptive electrostatic phase plate. Here, we present the design, fabrication, and characterization of an adaptive thin-film electrostatic phase plate, with gold electrodes supported on a thin silicon nitride membrane. Our design consists of six electrodes positioned as concentric and equidistant circles. We describe the fabrication of the phase plate, with particular focus on the less conventional steps involved. After careful process optimization, the fabricated phase plate features largely match the design, and approximately 95% of the 174 chips fabricated on each wafer show no visible defects in the active area. We characterize the functionality of the fabricated phase plate using both computational (comsol Multiphysics) and experimental (Fresnel imaging) methods. The phase profile reconstruction using the transport of intensity equation method generally follows the predicted phase profile. The desired quartic phase profile is partly achieved, although the presence of electrodes increase noise, and the electrode contacts introduce asymmetry. This opens up opportunities for further optimization before implementing the phase plates in a TEM for spherical aberration correction.

We investigate how feature-space dimensionality reshapes relaxation and correlation structure in a transverse-field Ising model with feature-conditioned, ferromagnetically biased bond disorder, analyzed via its anisotropic (two plus one)-dimensional classical counterpart. Using Suzuki–Trotter simulations of the mapped classical system, we study imaginary-time autocorrelators, coarse equal-time spatial summaries, and eigenvalue spectra of equal-time correlation matrices. Each spin is assigned a $d$-component feature vector, and a similarity scale $\hat{\sigma }$ controls how feature differences bias the distribution of bond signs. Increasing $d$ homogenizes pairwise feature distances and, together with $\hat{\sigma }$, tunes bond-sign heterogeneity within this positively biased ensemble. We find a systematic dynamical crossover from short-$\tau$ transients at low $d$ to multistep relaxation with long-lived plateaus at higher $d$, accompanied by a compression of equal-time correlation weight into fewer dominant spatial modes. Feature-conditioned disorder thus provides a controlled framework for studying how correlated disorder reshapes relaxation spectra in disordered Ising networks, with potential relevance to programmable Ising hardware.

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

Efficient and precise quantum state detection is essential for quantum information processing, serving as the backbone for high-fidelity initialization, real-time error correction, and scalable quantum operations. In particular, fluorescence-based detection, which is widely used in both neutral-atom and trapped-ion systems, must retain high fidelity under varying imaging conditions. Here, we present an adaptive convolutional neural network (CNN) that incorporates exposure time as a contextual input, enabling robust generalization across different exposure settings without retraining. To meet the stringent requirements of real-time quantum applications, we accelerate the model using a field-programmable gate array (FPGA), achieving an inference latency of $9.85\text{μ}\mathrm{s}$ at a 500 MHz clock frequency. Compared with state-of-the-art CPU and graphics processing unit (GPU) implementations, our hardware-accelerated design achieves an inference latency reduction of about $30\times$. These results highlight the potential for real-time context-aware quantum state detection, with a scalable model design that can incorporate additional experimental parameters. The proposed approach is well suited for integration in heterogeneous control architectures, supporting low-latency, resource-efficient, hybrid quantum-classical workflows.

Due to the comprehensive performance of the phase-change radio-frequency (rf) switch, it provides a promising idea for the next generation of alternative microwave switch. This work reports that the directly heated phase-change switches based on $\mathrm{Ge}\mathrm{Te}$/$\mathrm{Sb}$ superlatticelike (SLL) material have excellent switching and rf transmission performance. Compared to single-component phase-change materials, $\mathrm{Ge}\mathrm{Te}$/$\mathrm{Sb}$ SLL systems have lower thermal conductivity. As a functional layer of switching devices, the utilization rate of Joule heat can be improved, thus improving the switching speed of the device and reducing operating energy consumption. The contact area between the electrode and the phase-change material is reduced, and the switching structure is optimized to further improve the heating efficiency. The pulse width of the rf switch operating voltage is as low as 100 ns, with a total energy consumption of 758.5 nJ. It was found that the insertion loss of the phase-change switch unit is less than 0.76 dB, and the isolation is greater than 20.8 dB (0.1–67 GHz), respectively. Moreover, single-pole double-throw (SPDT) and single-pole four-throw (SP4T) phase-change rf switches can be fabricated by controlling the configuration between switching units. The present rf switches based on $\mathrm{Ge}\mathrm{Te}$/$\mathrm{Sb}$ SLL materials can be used in future wireless and 6G communication reconfiguration systems to switch between signal-processing modules to optimize signal quality and coverage.

With rapid progress being made in the development of platforms for quantum computation, there has been considerable interest in whether present-day and near-term devices can be used to solve problems of relevance. A commonly cited application area is the domain of quantum chemistry. While most experimental demonstrations of quantum chemical calculations on quantum devices have focused on the ground-state electronic energy of the system, other properties of the ground state, such as the electric dipole moment, are also of interest. Here, we employ the quantum-computed-moments method, based on the Lanczos cluster expansion, to estimate the dipole moment of the water molecule on an IBM Quantum superconducting quantum device. The noise-mitigated results agree with full-configuration-interaction calculations to within $0.03\pm 0.007$ D ($2\mathrm{\%}\pm 0.5\mathrm{\%}$), compared with direct expectation-value determination (i.e., variational quantum eigensolver, VQE) with errors of the order of 0.07 D (5%), even when the VQE calculation is performed without noise. This demonstrates that moments-based energy-estimation techniques can be adapted to noise-robust evaluation of nonenergetic ground-state properties of chemical systems. We emphasize that the dipole moment is chosen as an example of such a property, but the method applies to any property determined by one- or two-body interactions.

In this study, we explore the potential of magnetic vortices and antivortices as novel sources for generating nanometer-scale microwave fields. Utilizing a single-spin scanning magnetometer based on diamond NV centers, we investigated both static and dynamic magnetic properties of a thin Permalloy square, focusing on the magnetization and vortex core responses. While static images revealed the standard magnetic configuration, we detected gigahertz-frequency ac fields strongly localized at the vortex core, as confirmed through optically detected magnetic resonance contrast and Rabi frequency imaging. We attribute this observation to the dynamic circular motion of the vortex core and its associated oscillatory behavior. Simulations based on the Object Oriented MicroMagnetic Framework (oommf) and the Landau-Lifshitz-Gilbert equation demonstrate that vortices and antivortices can generate gigahertz microwave fields that are several orders of magnitude more intense than external driving fields, with spatial localization on the scale of tens of nanometers. This research opens new possibilities for generating powerful localized microwave fields that could effectively manipulate NV spin states, offering promising applications in quantum information processing and multi-NV quantum registers for advanced sensing.

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.

In materials known as failed insulators, electrical resistivity increases as temperature decreases, yet does not diverge—a phenomenon inconsistent with single-particle transport theories. We investigate the origin of this behavior by measuring shot noise in nanojunctions of nitrogen-doped $\beta$-$\mathrm{Ta}$, a prototypical failed insulator and an efficient spin-orbitronic material. Junctions as short as $8$ nm exhibit hot-electron shot noise, indicating strong electron interactions. We show that charge hopping mediated by electron interactions can account for the anomalous failed insulator properties. Based on our findings, we identify failed insulators as a distinct class of non-Fermi liquids, opening avenues for the studies of electron interactions and their applications.

Planar current-induced magnetization switching (CIMS) driven by spin-orbit torque (SOT) requires an in-plane uniaxial magnetic anisotropy (UMA), which can be induced by oblique-angle sputter deposition of the heavy-metal underlayer in heavy-metal/ferromagnet heterostructures. To enhance the SOT efficiency, we employ trilayer heterostructures of ($\mathrm{Ta}$ or $\mathrm{W}$)/$\mathrm{Co}$-$\mathrm{Fe}$-$\mathrm{B}$/$\mathrm{Pt}$, where the $\mathrm{Co}$-$\mathrm{Fe}$-$\mathrm{B}$ layer exhibits a UMA of 50 mT at a 2-nm thickness of $\mathrm{Ta}$ or $\mathrm{W}$. The magnetization reversal in Hall-bar devices is detected through unidirectional spin-Hall magnetoresistance for the type-$y$ geometry (easy-axis transverse to current) and planar Hall measurements for the type-$x$ geometry (easy-axis parallel to current). Both configurations exhibit CIMS with submicrosecond current pulses, reaching switching-current densities as low as $2\times {10}^{11}\mathrm{A}/{\mathrm{m}}^2$ for a $\mathrm{W}$ (4 nm)/$\mathrm{Co}$-$\mathrm{Fe}$-$\mathrm{B}$ (1.4 nm)/$\mathrm{Pt}$ (2 nm) stack with a UMA of 146 mT. Macrospin simulations reproduce the type-$y$ switching as coherent magnetization rotation, whereas the type-$x$ devices switch at much lower currents than predicted, indicating that nucleation and domain-wall propagation dominate reversal in this geometry. Our results show that combining oblique-angle deposition with easy-axis engineering enables deterministic, field-free switching, paving the way for future low-power spintronic devices.

We present a noninvasive feedback control scheme to access unstable periodic orbits in dynamic-mode atomic force microscopy. By utilizing this method we are able to explore the complete nonlinear frequency-response curves, including unstable branches between saddle-node bifurcations. The overall scheme is designed to be fast and derivative-free, which allows for simple experimental implementations. The control is closely related to phase-locked loop testing, a truncated Fourier discretization of periodic orbits, and an adaptive filter for online signal demodulation. We demonstrate the feedback control on various contact models for dynamic-mode atomic force microscopy and hope to motivate laboratory experiments by elucidating software and corresponding hardware implementations in detail.

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.