We present a detailed simulation and design framework for realizing traveling-wave parametric amplifiers (TWPAs) using the nonlinear kinetic inductance of disordered superconductors—in our case niobium-titanium-nitride ($\mathrm{Nb}\mathrm{Ti}\mathrm{N}$). These kinetic inductance TWPAs (KITs) operate via three-wave mixing to achieve high broadband gain and near-quantum-limited noise. Representative fabricated devices—realized using an inverted microstrip, dispersion-engineered, artificial transmission line—demonstrate power gains above 25 dB and bandwidths beyond 3 GHz, and achieve ultimate system noise levels of 1.1 quanta, even when operated with no magnetic shielding. These performance metrics are competitive with state-of-the-art Josephson-junction-based TWPAs, while offering further advantages: KITs require simpler fabrication, provide more than 3 orders of magnitude higher dynamic range (${\mathrm{IIP}}_1=-68\mathrm{dBm}$, ${\mathrm{IIP}}_3=-55\mathrm{dBm}$), and offer higher magnetic field resilience. These features make KITs an attractive technology for highly multiplexed readout of quantum information and superconducting detector systems.

Self-healing in a representative self-organizing physical system is presented based on three-dimensional nonlinear numerical simulations. The significance of nonphysical spatial boundaries of surface energy discontinuities that create static interfaces in liquid-liquid phase separation (LLPS) is investigated. The spatial boundaries and LLPS are reminiscent of “coazervation” in biological cells as well as the emergence of “compactons” with finite support. A subspace of the system inside these spatial boundaries can be designed to acquire a permanent structural memory of a nanodroplet that enables it to regulate the organization of the liquid as a nanodroplet of a specific shape and size. This memory is an outcome of the localization process of liquid nanodroplets in the subspace and leads to its healing against any damage during and after LLPS. Interestingly, these boundaries can also ensure the delocalization of a subspace in a mix of neighboring subspaces holding localized nanodroplets, initiating a strong bias in the direction of liquid flow. Thus, a seemingly simple system emerges as a complex network of interacting localized nanodroplets separated by the spatial boundaries. The methodology outlined in this paper is robust and quite general and will be found applicable in various liquids, gels, and polymers under the appropriate force fields. This study is crucial in developing self-repairing nanodevices, soft nanomachines, and nanomedicines and understanding the transport processes in biological and active-matter systems.

We present an analytical model for power transfer in a magnetoelectric-film bulk-acoustic resonator (FBAR) comprising a piezoelectric-magnetostrictive bilayer. The model describes the power flow between the elastic and magnetic systems, quantifying the transduction efficiency when the FBAR operates as a magnetic transducer. By applying the model to example systems using piezoelectric $\mathrm{Sc}\mathrm{Al}\mathrm{N}$ and magnetostrictive $\mathrm{Co}\mathrm{Fe}\mathrm{B}$, $\mathrm{Ni}$, or Terfenol-D layers, we demonstrate the potential for achieving high efficiencies in magnetoelectric transducers, rendering them ideal for efficient ferromagnetic resonance excitation. The validity of the model’s assumptions is confirmed through comparison with a numerical finite-element resonator model in comsol. The finite-element model further enables a comprehensive study of the resonator’s dynamic behavior, including transient and steady-state regimes, and the identification of resonant frequencies within the system.

The light-cone transform (LCT) is a mathematical technique used in non-line-of-sight (NLOS) imaging; it is used to reconstruct hidden objects by analyzing the temporal evolution of scattered light paths. Wiener filtering is commonly employed to reduce noise that often distorts these three-dimensional reconstructions; however, its low-pass nature may attenuate high-frequency details, thereby compromising reconstruction accuracy and overall quality. In this work, we propose a learned light-cone transform (${\mathrm{L}}^2\mathrm{CT}$) method that rapidly converts transient data into three-dimensional hidden objects while preserving intricate physical details. We introduce a high-frequency-enhanced Wiener-filtering technique that retains low-frequency components while capturing high-frequency details through learnable parameterization, enabling more accurate reconstruction of hidden objects. Additionally, we integrate spatiotemporal feature-extraction and volume-projection modules to create an end-to-end NLOS image-reconstruction network. Extensive experiments demonstrate that our method significantly improves LCT reconstruction on real-world datasets captured by different NLOS imaging systems. The ${\mathrm{L}}^2\mathrm{CT}$ method surpasses existing approaches, achieving a 5-dB improvement in peak signal-to-noise ratio over the LCT and reaching state-of-the-art performance in NLOS reconstruction.

We characterize an efficient optically heated neutral atom source for ion trapping. We observe loading rates of up to $24(3){\mathrm{s}}^{-1}$ with heating powers below 85 mW, and demonstrate loading of a single ion in under 30 s with 41.4(4) mW of optical power in a room-temperature ion-trap system with an ionization probability of $1.50(5)\times {10}^{-5}$. We calibrate a thermal model for the source’s internal temperature by imaging the fluorescence of a collimated flux of neutral calcium that effuses from the source at various optical heating powers. We show that the thermal performance of this source is mainly limited by radiative losses. We explore the effect of second-stage photoionization laser power on the loading rate, and identify a path beyond the loading rates reported in this study. We predict that this source is also well suited to a wide range of metals used in ion trapping.

Superconducting circuits are being used for large-scale quantum devices, and a major challenge is to perform accurate numerical simulations of device parameters. One of the most advanced methods for analyzing superconducting circuit designs is the energy-participation-ratio (EPR) method, which constructs quantum Hamiltonians based on the energy distribution extracted from classical electromagnetic simulations. In the EPR approach, we extract linear terms from finite-element simulations and add nonlinear terms using the energy participation ratio extracted from the classical simulations. However, the EPR method relies on a low-order expansion of nonlinear terms, which is prohibitive for accurately describing highly anharmonic circuits. An example of such a circuit is the fluxonium qubit, which has recently attracted increasing attention due to its high lifetimes and low error rates. In this work, we extend the EPR approach to effectively address highly nonlinear superconducting circuits, and, as a proof of concept, we apply our approach to a fluxonium qubit. Specifically, we design, fabricate, and experimentally measure a fluxonium qubit coupled to a readout resonator. We compare the measured frequencies of both the qubit and the resonator to those extracted from the EPR analysis, and we find an excellent agreement. Furthermore, we compare the dispersive shift as a function of external flux obtained from experiments with our EPR analysis and a simpler lumped-element model. Our findings reveal that the EPR results closely align with the experimental data, providing more accurate estimations compared to the simplified lumped-element simulations.

In this article, we propose a passive parity-time ($\mathcal{P}\mathcal{T}$)-symmetric metasurface mirror composed of only a lossy stepped waveguide per period, exhibiting perfect retroflection for positive incidence (PI) but perfect absorption for negative incidence (NI) at the exceptional point. The mechanism underlying the extreme asymmetry is the unilateral excitation of the evanescent wave and the waveguide mode. In addition, by combining two pieces of the established passive $\mathcal{P}\mathcal{T}$-symmetric metasurface mirrors, the incident wave for NI absorbed by one of the established passive $\mathcal{P}\mathcal{T}$-symmetric metasurface mirrors can be recovered by the other. Thus, a passive $\mathcal{P}\mathcal{T}$-symmetric metasurface with balanced “loss” and “gain” is constructed, exhibiting perfect retroflection for PI but perfect transmission for NI. This work extensively simplifies the design of the $\mathcal{P}\mathcal{T}$-symmetric metasurface, and may stimulate the development of asymmetric response engineering in compact acoustic devices.

The nitrogen-vacancy ($\mathrm{N}$-V) center in diamond provides a robust, solid-state platform for magnetic field measurements at room temperature. To harness its potential in inspecting inaccessible regions, here we present a compact endoscopic configuration of a $\mathrm{N}$-V diamond–based magnetometer. The endoscopic magnetometer was developed by integration of a large-core optical fiber with a bulk $\mathrm{N}$-V diamond for laser excitation and photoluminescence (PL) collection. The diamond and fiber were specially shaped to enhance PL collection through the fiber. Additionally, a three-dimensionally-printed endoscope head was used to facilitate alignment of the bias magnetic field along the $\mathrm{N}$-V axis. A magnetic field sensitivity of approximately 3 nT/√Hz at 20 Hz was achieved by continuous-wave magnetometry measurements. The endoscope diameter was restricted to 10 mm to match the dimensions of most commercial endoscopes. The magnetic field nonuniformity caused by the small separation between the diamond and the magnet in the endoscope head limited the overall sensitivity. It could be further improved to 0.85 nT/√Hz at 20 Hz by use of a magnet placed at a sufficient distance outside the endoscope head. Our endoscopic design is mechanically stable and provides additional opportunities for integrating other functionalities into the probe head as needed.

Synthetic antiferromagnets offer rich magnon energy spectra in which optical and acoustic magnon branches can hybridize. Here, we demonstrate a broken-intrinsic-symmetry-induced coupling of acoustic and optical magnons in a synthetic ferrimagnet consisting of two dissimilar antiferromagnetically interacting ferromagnetic metals. Two distinct magnon modes hybridize at degeneracy points, as indicated by an avoided level crossing. The avoided level crossing gap depends on the interlayer exchange interaction between the magnetic layers, which can be controlled by adjusting the nonmagnetic interlayer thickness. A large avoided level crossing gap of 3.9 GHz is revealed, exceeding the coupling strength that is typically found in other magnonic hybrid systems based on a coupling of magnons with photons or magnons with phonons.

Highly efficient deep-blue photoluminescence (PL) of two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs) is critical for optoelectronic applications. However, the formation of lattice distortion–induced self-trapped excitons (STEs) and nonradiative recombination significantly impede the narrow-band deep-blue PL in 2D HOIPs. In the work reported here, pressure-induced enhanced deep-blue PL is achieved in 2D $(\mathrm{FIPA}{)}_2{\mathrm{Pb}\mathrm{Br}}_4$ (FIPA is 1,1,1-trifluoropropan-2-amine) perovskite. Notably, the narrow-band deep-blue PL intensity exhibits a 743-fold increase at 3.09 GPa. In situ Raman experiments and density-functional theory calculations confirm that enhanced intermolecular interaction forces and reduced inorganic $[{\mathrm{Pb}\mathrm{Br}}_6{]}^{4-}$ octahedral distortion under pressure lead to delocalization of STEs into free excitons (FEs). Furthermore, pressure-enhanced $\mathrm{C}$—$\mathrm{H}$···$\mathrm{Br}$ and $\mathrm{N}$—$\mathrm{H}$···$\mathrm{Br}$ hydrogen-bonding interactions inhibit the nonradiative recombination of FEs and STEs, which significantly increases the PL efficiency. This work may pave new pathways for the development of 2D perovskites with efficient and ultrapure deep-blue PL for further optoelectronic applications.

The emergence of sliding ferroelectricity has opened new avenues for the development of two-dimensional (2D) ferroelectrics. However, the fundamental mechanisms governing sliding-induced multistate ferroelectricity and its correlation with optoelectronic properties remain poorly understood. Using first-principles calculations, we systematically explore the stable multistate ferroelectricity and photoelectric conversion properties in a van der Waals (vdW) 1T′-${\mathrm{Re}\mathrm{S}}_2$ bilayer with ground-state symmetry under sliding manipulation. Our results not only identify diverse and robust sliding ferroelectricity but also reveal the exceptional stability of four distinct polarization states. Notably, we demonstrate that interlayer sliding facilitates reversible polarization switching via an ultralow energy barrier. Moreover, sliding-modulated band-edge shifts and interfacial charge redistribution synergistically drive dynamic transitions between type-II and type-V band alignments during multistate switching, yielding outstanding photovoltaic performance with a theoretical power conversion efficiency of up to 23.63%. These findings establish 2D vdW 1T′-${\mathrm{Re}\mathrm{S}}_2$ as a promising candidate for reconfigurable multistate ferroelectrics and high-efficiency sliding-driven photovoltaics.

Current-induced spin-orbit torque (SOT) offers a promising approach for manipulating perpendicular magnetization in spintronic devices, enabling low-power and ultrafast applications. However, deterministic SOT switching typically requires an external in-plane magnetic field to break symmetry, complicating high-density device integration. Here, we demonstrate field-free SOT switching in the perpendicular ferrimagnet CoGd by inducting tilted magnetic anisotropy (TMA) via thermal annealing under an in-plane magnetic field. The TMA’s tilt angle is tunable by adjusting the annealing field strength, allowing deterministic switching when the in-plane TMA component aligns with the spin-polarization direction. This method leverages the low stray field, ultrafast spin dynamics, and high operation frequencies of ferrimagnet, offering a scalable solution for advanced spintronic devices.

We report on an optical magnetometer enhanced by vacuum-squeezed light, employing an Mx magnetometer based on ${}^{87}\mathrm{Rb}$ vapor in a micrometer-scale cell (approximately 100 µm). Using the well-established polarization self-rotation effect in a room-temperature ${}^{87}\mathrm{Rb}$ vapor cell, we achieve 4 dB of vacuum squeezing within the noise spectral window of 100 Hz to several MHz, corresponding to 4.5-dB squeezing when accounting for optical losses. Leveraging this level of squeezing, we demonstrate a magnetic-field sensitivity of approximately 1 pT√Hz. The combination of vacuum-squeezed light and micrometer-scale vapor cells paves the way for compact, low-power-consumption atomic sensors with enhanced performance.

Thermal boundary conductance (TBC) between silicon and diamond influences heat removal in next-generation electronics where $\mathrm{Si}$ and diamond serve as substrates or channels. In this paper, we report simulations of $\mathrm{Si}$/diamond interfaces using machine-learning interatomic potential (MLIP)-driven molecular dynamics. MLIPs trained on ab initio $\mathrm{Si}$/diamond heterostructures faithfully reproduce interfacial $\mathrm{Si}$—$\mathrm{C}$ bonding and the phonon dispersions of bulk $\mathrm{Si}$ and diamond, whereas the classical Tersoff potential fails to capture these accurately. After quantum correction, the room-temperature TBC of a bare $\mathrm{Si}$/diamond interface is $130\mathrm{MW}{\mathrm{m}}^{-2}{\mathrm{K}}^{-1}$, in close agreement with experiment (approximately $140\mathrm{MW}{\mathrm{m}}^{-2}{\mathrm{K}}^{-1}$), and far below predictions from Tersoff molecular dynamics ($310\mathrm{MW}{\mathrm{m}}^{-2}{\mathrm{K}}^{-1}$) or a first-principles phonon-dispersion diffuse mismatch model ($205\mathrm{MW}{\mathrm{m}}^{-2}{\mathrm{K}}^{-1}$). Adding a 1-nm-thick ${\mathrm{Si}\mathrm{N}}_x$ interlayer approximately doubles the TBC to $275\mathrm{MW}{\mathrm{m}}^{-2}{\mathrm{K}}^{-1}$, and replacing ${\mathrm{Si}\mathrm{N}}_x$ with amorphous carbon further increases it to $350\mathrm{MW}{\mathrm{m}}^{-2}{\mathrm{K}}^{-1}$. The density of states analysis reveals that the interlayer supplies a large portion of intermediate phonon modes that bridge $\mathrm{Si}$ and diamond. Finite-element device simulations indicate that these TBC improvements measurably reduce peak chip temperature. These results highlight interfacial-layer engineering as a potential route to improved thermal management in wide-band-gap electronics.

Spintronic devices have emerged as promising candidates for neuromorphic computing, owing to their nonvolatility and plasticity that enable the emulation of synaptic and neuronal functions. Compared with conventional ferromagnets, synthetic antiferromagnets (SAFs) exhibit unique and fascinating properties, including negligible stray field, ultrafast spin dynamics, and chiral interlayer coupling. However, the potential applications of SAF, particularly those with chiral coupling, in neuromorphic computing remain relatively unexplored, which motivates the present experimental study. Here, using a chirally coupled $\mathrm{Co}/\mathrm{Ru}/{\mathrm{Fe}}_{0.60}{\mathrm{Tb}}_{0.40}$ SAF structure with noncollinear magnetization configuration, we demonstrate that linear and nonlinear memristive switching, driven by spin-orbit torque (SOT) in different operating regimes, can effectively emulate biological synapses and neurons, respectively. In particular, current-induced SOT switching in chirally coupled SAFs exhibits stable and pronounced multilevel switching behavior that current pulses can precisely tune. Using experimentally derived synapse and neuron parameters, we further simulate a three-layer fully connected neural network for handwritten digit recognition, achieving an accuracy of over 94%. This work establishes chirally coupled SAF as a highly promising building block for high-performance neuromorphic spintronic devices.

Nonlinear dynamical systems with continuous variables can be used for solving combinatorial optimization problems with discrete variables. Numerical simulations of them are also useful as heuristic algorithms with a desirable property, namely, parallelizability, which allows us to execute them in a massively parallel manner, leading to ultrafast performance. However, the dynamical-system approaches with continuous variables are usually less accurate than conventional approaches with discrete variables such as simulated annealing. To improve the solution accuracy of a quantum-inspired algorithm called simulated bifurcation (SB), which was found from classical simulation of a quantum nonlinear oscillator network exhibiting quantum bifurcation, here we generalize it by introducing nonlinear control of individual bifurcation parameters and show that the generalized SB (GSB) can achieve surprisingly high performance, namely, almost 100% success probabilities for some large-scale problems. As a result, the time to solution for a 2000-variable problem is shortened to 10 ms by a GSB-based machine, which is 2 orders of magnitude shorter than the best known value, 1.3 s, previously obtained by an SB-based machine. To examine the reason for the ultrahigh performance, we investigated chaos in the GSB changing the nonlinear-control strength and found that the dramatic increase of success probabilities happens near the edge of chaos. That is, the GSB can find a solution with high probability by harnessing the edge of chaos. This finding suggests that dynamical-system approaches to combinatorial optimization will be enhanced by harnessing the edge of chaos, opening a broad possibility for physics-inspired approaches to combinatorial optimization.

Hybrid quantum systems integrating spin qubits and superconducting qubits have emerged as promising candidates for scalable quantum information processing. In hybrid architectures, the development of high-fidelity quantum buses is a crucial but challenging element. Here we design a quantum bus that utilizes engineered virtual photons to couple spin and superconducting modules. We tailor the driving pulse to accelerate the evolution of the hybrid system in a noncyclic geometric phase way. We demonstrate the enhanced functionality of the quantum bus in three ways. First, we propose a scheme for realizing universal gate operation between different modules in a short time of 40 ns with 99.05% fidelity, exceeding the fault-tolerance threshold. Second, we theoretically demonstrate the preparation of remote entanglement between different modules with a fidelity of 99.21%, a valuable resource for hybrid architectures. Finally, we show the robustness of this approach to control parameter imperfections compared to usual dynamical pulses. These results provide a toolbox for fast, high-fidelity, and robust quantum information processing on hybrid platforms.

Quantum processors require rapid and high-fidelity simultaneous measurements of many qubits. While superconducting qubits are among the leading modalities toward a useful quantum processor, their readout remains a bottleneck. Traditional approaches to processing measurement data often struggle to account for crosstalk present in frequency-multiplexed readout, the preferred method to reduce the resource overhead. Recent approaches to address this challenge use neural networks to improve the state-discrimination fidelity. However, they are computationally expensive to train and evaluate, resulting in increased latency and poor scalability as the number of qubits increases. We present an alternative machine-learning approach based on next-generation reservoir computing that constructs polynomial features from the measurement signals and maps them to the corresponding qubit states. This method is highly parallelizable, avoids the costly nonlinear activation functions common in neural networks, and supports real-time training, enabling fast evaluation, adaptability, and scalability. Despite its lower computational complexity, our reservoir approach is able to maintain high qubit-state-discrimination fidelity. Relative to traditional methods, our approach achieves error reductions of up to 50% and 11% on single- and five-qubit datasets, respectively, and delivers up to $2.5\times$ crosstalk reduction on the five-qubit dataset. Compared with recent machine-learning methods, evaluating our model requires $100\times$ fewer multiplications for single-qubit and $2.5\times$ fewer for five-qubit models. This work demonstrates that reservoir computing can enhance qubit-state discrimination while maintaining scalability for future quantum processors.

We present a technical approach to determine whether a nuclear weapon experiment was a supercritical test—tests with a self-sustaining, growing fission chain reaction. It is based on on-site gamma spectroscopy measurements taken of test debris weeks or months later. Analyzing simulated spectra, we can define thresholds above which intensity ratios of select fission products and plutonium-239 emission lines reliably indicate supercriticality. Within ranges that include most practical scenarios, our method is independent of test parameters and sensitive information, including age, shape, and mass of the tested fissile material, initial fissions at experiment’s start and time passed until inspection. Our method can reliably detect any supercritical tests with an explosive yield greater than 38 g TNT equivalent. This method could be applied as a confidence-building measure among nuclear-armed states willing to prohibit supercritical tests.