We report the fabrication of a barium magnesium fluoride (${\mathrm{Ba}\mathrm{Mg}\mathrm{F}}_4$) whispering-gallery-mode (WGM) microresonator demonstrating an intrinsic quality factor ($Q_0$) of about $2\times {10}^9$ at $1.55\text{μ}\mathrm{m}$, corresponding to a loaded quality factor ($Q_L$) of about $1\times {10}^9$ under critical coupling. At $398\mathrm{nm}$, the resonator exhibits a quality factor ($Q_L$) of $1.25\times {10}^8$ measured in the undercoupled regime, highlighting its potential for ultraviolet (UV) photonics. This performance was achieved in a 2.23-$\mathrm{mm}$-diameter resonator produced via a multistage process combining single-point diamond turning, chemomechanical polishing, and high-temperature annealing, yielding a surface roughness of approximately 1-$\mathrm{nm}$ RMS. Photoluminescence measurements under UV excitation revealed broadband visible emission, confirming the high optical quality of the crystal. When pumped in the near-infrared range, the resonator acted as a nonlinear element enabling the formation of Kerr optical frequency combs with a spectral width of about 30 nm and a line spacing of 42 GHz in the normal-dispersion regime. When incorporated into a fiber-laser cavity, it supported the generation of a frequency comb with a bandwidth of about 20 nm and a mode spacing of approximately 1 GHz at a pump power threshold of 86 mW. These results establish ${\mathrm{Ba}\mathrm{Mg}\mathrm{F}}_4$ WGM microresonators as a promising platform for nonlinear photonics across a broad spectral range, from the UV to the near-infrared.

Lowering the resource overhead needed to achieve fault-tolerant quantum computation is crucial to building scalable quantum computers. We show that adapting conventional maximum likelihood (ML) decoders to a small subset of efficiently learnable physical error characteristics can significantly improve the logical performance of a quantum error-correcting code. Specifically, we leverage error information obtained from efficient characterization methods based on cycle error reconstruction (CER), which yields Pauli error rates on the $n$ qubits of an error-correcting code. Although the total number of Pauli error rates needed to describe a general noise process is exponentially large in $n$, we show that only a few of the largest few Pauli error rates are needed and that a heuristic technique can complete the Pauli error distribution for ML decoding from this restricted dataset. Using these techniques, we demonstrate significant performance improvements for decoding quantum codes under a variety of physically relevant error models. For instance, with CER data that constitute merely 1% of the Pauli error rates in the system, we achieve a 10 times gain in performance compared with the case where decoding is based solely on the fidelity of the underlying noise process. Our conclusions underscore the promise of recent error characterization methods for improving quantum error correction and lowering overheads.

We present a differential technique for vector magnetic sensing based on a cold-atom cloud in a magnetic quadrupole trap. An external homogeneous magnetic field displaces the trap center in a direction and with a magnitude proportional to the field. By reversing the quadrupole polarity between experimental shots and comparing the resulting cloud positions, we extract a differential displacement signal that is free from common-mode effects such as gravity and weak magnetic field inhomogeneities. The signal is directionally proportional to the external field and requires only absorption imaging, without spectroscopic interrogation. Assuming micron-scale position resolution, the technique enables field resolution at the milligauss level. It offers a practical tool for field compensation in magnetically sensitive experimental stages, bridging operational regimes from Earth-level fields to atomic magnetometry. A straightforward extension to full three-dimensional sensing is possible with only a minimal addition to standard cold-atom infrastructure.

Laser-generated surface acoustic waves (SAWs) facilitate efficient information processing in modern spintronics and magnonics. The ability to tune the SAW parameters such as the amplitude is crucial to achieve acoustic control over magnonic properties. Such tunability can be achieved in phase-changing magnetic materials that accommodate both spin waves and SAWs. A promising material is the $\mathrm{Fe}$-$\mathrm{Rh}$ alloy, a metallic antiferromagnet at room temperature that undergoes a phase transition to the ferromagnetic state accompanied by a crystal lattice expansion at 370 K. This transition can also be induced by femtosecond laser pulses. In this paper, we use the phase transition in a 60-nm $\mathrm{Fe}$${}_{49}$$\mathrm{Rh}$${}_{51}$ film to optically generate pulses of Gigahertz quasi-Rayleigh SAWs. We detect them via the photoelastic effect and show that the lattice transformation during the phase transition is a dominant strain-generation mechanism for above-threshold excitation. The weight of this contribution rises as the sample is heated closer to the antiferromagnetic-ferromagnetic transition temperature and “switches off” when heated above it, allowing for control of the SAW amplitude. A model based on thermodynamic parameters of $\mathrm{Fe}$${}_{49}$$\mathrm{Rh}$${}_{51}$ shows that the lattice transformation occurring within 95 ps effectively contributes to SAW generation happening on a comparable timescale, while nonequilibrium fast kinetics of the phase transition does not.

Point defects, including intentional dopants, compensating impurities, and native point defects, as well as extended defects have a large impact on semiconductor devices and their performance. Some point defects are intentionally incorporated as dopants to provide free carriers or intentionally compensate material, and others serve as nonradiative and scattering centers that are deleterious to device performance. Shockley-Read-Hall (SRH) recombination is commonly used to determine the recombination rate associated with point defects when simulating devices. As the SRH model was developed treating only the capture processes of carriers with defects that scale linearly with carrier density, such as multiphonon emission and radiative capture, the defect-mediated recombination rates and currents it predicts are accurate only in the low-carrier-density regimes. Our recent experimental observation of hot electrons generated by trap-assisted Auger-Meitner recombination (TAAR) from a shallow impurity [Phys. Rev. B 111, 155308 (2025)], whose emission currents had a strong dependence on the operating device currents, raises a question regarding whether only a single type of capture and emission process can be ascribed to a given defect, or instead if several possible capture and emission processes with an appreciable rate must be considered for the same defect. In this work, we extend the conventional SRH analysis to include trap-assisted Auger-Meitner processes in defect-assisted recombination cycles (DARCs). The inclusion of TAAR processes in DARCs demonstrates that defect-mediated recombination rates and currents can scale quadratically with carrier density and can significantly deviate from the predictions of the conventional SRH model, especially in the higher-carrier-density regimes. We perform a case study for the recombination rates involving the ${\mathrm{Ca}}_{\mathrm{Ga}}$ impurities in $\mathrm{Ga}\mathrm{N}$ using our formalism, where ${\mathrm{Ca}}_{\mathrm{Ga}}$ is chosen because hot electrons from ee TAAR involving ${\mathrm{Ca}}_{\mathrm{Ga}}$ have been measured using electron emission spectroscopy. Implementation of DARCs using the analytical framework presented in this article can result in the increased accuracy of simulated defect-mediated recombination rates, especially for situations where there are large excess carrier densities such as in quantum-well-based light-emitting diodes and lasers.

Artificial atoms nonlocally coupled to waveguides—the so-called giant atoms—offer opportunities for the control of light and matter. In this work, we show how to use an array of nonlocally coupled transmon “molecules” to engineer a passive photonic controlled gate for waveguide photons. In particular, we show that a conditional elastic phase shift between counterpropagating photons arises from the interplay between direction-dependent couplings, engineered through an interplay of nonlocal interactions and molecular binding strength; and the nonlinearity of the transmon array. We analyze the conditions under which a maximal $\pi$-phase shift—and hence a CZ gate—is obtained, and characterize the gate fidelity as a function of key experimental parameters, including finite transmon nonlinearities, emitter spectral inhomogeneities, and limited cooperativity. Our work opens the use of giant atoms as key elements of microwave photonic quantum computing devices.

In recent years, multilayer structures that tune topological phases through the regulation of the layer degree of freedom have attracted significant attention. Compared with monolayer systems, multilayer systems with interlayer coupling can host a richer variety of topological phases, among which those with heterogeneous layers remain insufficiently explored. In this paper, based on the two-dimensional Su-Schrieffer-Heeger model, we investigate bilayer and trilayer heterostructures composed of vertically stacked layers with different topological properties. We find that variations in the interlayer coupling strength can induce topological phase transitions in the system, significantly altering the number of corner states in these topological structures. The evolution of the number of corner states is further directly demonstrated by calculating the multipole chiral numbers. We performed acoustic simulations to verify the variation in the number of corner states in bilayer and trilayer heterostructures, and further proposed a new scheme for constructing topological bound states in the continuum. Our work experimentally demonstrates the effectiveness of this phenomenon in acoustic systems and offers a practical route to realize reconfigurable acoustic devices for wave-energy confinement, topological filtering, and sensing, leveraging controllable layer coupling in compact architectures.

Retrieving information through disordered scattering systems remains a major challenge in wireless communication and sensing due to the severe distortion and attenuation caused by multiple scattering. Here, we demonstrate robust focusing through random media using a leaky cavity made programmable by reconfigurable metasurfaces. We show that the leaky cavity leverages backscattering from the disordered medium to enhance the degree of control over the transmitted field. We achieve an enhancement in focused intensity by an order of magnitude compared to that of a horn antenna. We further demonstrate robust communication through the medium using low-power amplitude-modulated signals and finally implement a backscatter communication scheme in which the same cavity illuminates and decodes data from a passive reflectivity-modulating target embedded within the disorder. Our results highlight the potential for reliable communication and sensing in complex environments.

Accurate ground-state calculations on noisy quantum computers are significantly constrained by restricted Ansatz expressivity and unavoidable hardware errors. We introduce a hybrid quantum-classical framework that simultaneously addresses these challenges. Our method systematically purifies noisy two-electron reduced density matrices from quantum devices by enforcing $N$-representability conditions through efficient semidefinite programming, guided by a norm-based distance constraint to the experimental data. To implement this constraint, we develop a hardware-efficient calibration protocol based on Clifford circuits. We demonstrate near full configuration-interaction accuracy for ground-state energies of ${\mathrm{H}}_2$, $\mathrm{Li}\mathrm{H}$, and ${\mathrm{H}}_4$, and compute precise scattering intensities for ${\mathrm{C}}_6{\mathrm{H}}_8$ on noisy hardware. This approach surpasses conventional methods by simultaneously overcoming both Ansatz limitations and hardware noise, providing a framework for quantum advantage and offering an alternative for reliable simulations of complex molecular systems on noisy devices.

Exquisite control of electron beam energy is required for many electron spectroscopy and imaging applications. For both continuous and pulsed beams, the beam energy spread is fundamentally limited by the electron source, and is typically a sizable fraction of an electron-volt. In this paper, we present a means to reduce electron beam energy spread after emission to the level of a few tens of millielectron-volts rms using femtosecond photoemission and an interaction with laser-derived single- to few-cycle terahertz (THz) radiation. We show analytically and in particle tracking simulations that this interaction can remove energy spread stored in both the transverse and longitudinal degrees of freedom. We analytically formulate the limit of energy spread that this technique can achieve, and map the nonideal effects arising at high frequencies. The interaction is mediated by the beam’s passage through a mirror which is reflective of THz radiation but allows transmission of the majority of the electron beam (e.g., a wire mesh). This method then only requires beam current losses of a few tens percent, far smaller than what is achieved in prism and slit-based electron monochromators.

Fast, reprogrammable control over the transverse structure of light beams plays an essential role in applications such as structured illumination microscopy, optical trapping, and quantum information processing. Existing technologies, such as liquid-crystal-on-silicon spatial light modulators or digital micromirror devices, suffer from limited refresh rates, low damage thresholds, and high insertion loss. Acousto-optic modulators (AOMs) address these limitations and enable rapid phase and amplitude modulation controlled by the amplitude and frequency of the rf waveform. By effectively mapping the temporal rf waveforms onto the spatial diffraction patterns of the optical field, individual AOMs have been shown to generate one-dimensional spatial modes at a pixel refresh rate of nearly 20 MHz. Here we extend this concept to enable fast modulation in a two-dimensional (2D) space using a double-AOM scheme and show the generation of 2D Hermite-Gaussian (${\mathrm{HG}}_{nm}$) modes. Numerical simulations demonstrate that one can improve the fidelity to a desired amplitude and phase pattern for the diffracted beam by optimizing the amplitude and frequency of the input rf waveform. With optimized rf input, our experimentally generated HG modes have an average mode fidelity of 81%, while the highest-order mode generated, ${\mathrm{HG}}_{53}$, maintains a fidelity of 56%.

Exceptional points (EPs) in non-Hermitian photonics offer singular sensitivity enhancements but have thus far been realized almost exclusively in spatially engineered platforms with fixed geometries and limited tunability. Here we extend EP physics into the temporal domain by introducing balanced gain-loss modulation in a photonic time crystal (PTC). A time-periodic refractive-index modulation $n(t)=n_0+\delta n\cos (\mathrm{\Omega }t)$ generates an effective non-Hermitian Floquet Hamiltonian that supports coalescence of quasieigenmodes in frequency space, constituting a genuine temporal exceptional point. Using a reduced two-mode model for the dominant frequency sidebands, we derive a non-Hermitian dimer Hamiltonian $H_{\mathcal{P}\mathcal{T}}(\mathrm{\Delta },\gamma ,\kappa )$ that is strictly $\mathcal{P}\mathcal{T}$ symmetric for $\mathrm{\Delta }=0$ and identify the exact EP condition. Numerical analysis reveals the associated Riemann-sheet topology, mode exchange and Berry-phase accumulation upon encirclement of the EP, and the characteristic $\sqrt{\epsilon }$ perturbation response indicative of enhanced sensing. We further construct a non-Hermitian transmission model that is exact within the reduced two-mode description, compute the Cramér-Rao bound (CRB) for temperature estimation under an explicit noise model, and show that EP-enhanced sensitivity persists when compared to a linewidth-matched Hermitian reference under identical resource constraints. Monte Carlo simulations confirm that the CRB is saturable using spectral measurements. These results establish temporal non-Hermiticity as an alternative paradigm for dynamically reconfigurable, broadband, and geometry-independent exceptional-point photonics.

We consider the problem of fast charging a cavity-Heisenberg spin-chain quantum battery, using as control functions an external classical field driving the cavity and the coupling between the cavity and the chain. To find the optimal controls, we employ two deep learning models, a neural network and a recurrent neural network, with recurrent connections that make it suitable for modeling sequential data. In all simulations performed for open and closed quantum system configurations, the recurrent model outperforms the simple neural network. The final stored energy achieved with the recurrent model for the closed system at short times is larger than that obtained in a recent work with reinforcement learning in much longer times. For the various open-system configurations studied, the obtained stored energies for short durations are higher than those achieved in the same work with reinforcement learning for the same durations. Regarding the optimal controls, for the closed system, both the coupling and the drive are modulated during the interval of interest, while for the open system, it seems that the optimal strategy is to fix the drive at its maximum possible absolute value to quickly insert energy into the cavity, which is transferred to the battery by modulating the coupling appropriately. The present work adds the recurrent neural-network model to the arsenal of machine-learning methods used for the control of complex quantum systems. In addition, the proposed setting is more attractive for potential implementation since the cavity field starts from the vacuum state and not an excited Fock state as in other related studies.

Quantum machine learning (QML) has emerged as a promising framework for exploring how quantum dynamics may enhance data processing tasks. Here we investigate quantum extreme-learning machines (QELMs), a quantum analog of classical extreme-learning machines in which training is restricted to the output layer. Our architecture combines dimensionality reduction (via principal component analysis or autoencoders), quantum state encoding, evolution under an XX Hamiltonian, and projective measurement to produce features for a classical single-layer classifier. By analyzing the classification accuracy as a function of evolution time, we observe a sharp transition between low- and high-accuracy regimes, followed by saturation. Remarkably, the saturated performance is comparable to that obtained using Haar-random unitaries that generate maximally complex dynamics, even though the XX model is integrable and local. Our results indicate that this increase in performance correlates with the onset of entanglement, which improves the embedding of classical data in Hilbert space and leads to more separable clusters in measurement probability space. Thus, moderate entanglement can contribute positively to the structure of the data representation, improving learnability without necessarily implying quantum computational advantage. For the image-classification tasks studied here, namely the Modified National Institute of Standards and Technology (MNIST), Fashion-MNIST, and CIFAR-10 datasets, the relevant evolution time is consistent with information exchange over short distances and, within the explored system sizes, does not show evidence of scaling with the full system size. This suggests that QELM performance in this regime relies only on limited entanglement and remains compatible with efficient classical simulation. Our results clarify how local quantum dynamics and moderate quantum correlations are already sufficient to generate useful feature representations for learning.

Recently, cladding-free waveguide systems without nearest-neighbor crosstalk have opened a path toward the miniaturization of photonic integrated circuits. However, there is still a prominent chance of crosstalk between next-nearest-neighbor channels, which severely degrades signal integrity and limits device performance. To overcome this challenge, we propose a deep-learning-based design strategy that further suppresses next-nearest-neighbor crosstalk below −20 dB across the operational bandwidth, thereby maintaining good signal isolation across almost all channels in the system. This approach offers a scalable solution for high-density photonic circuits and phased arrays, facilitating potential applications including optical interconnects, quantum simulations, and radar antennas.

We present a method for improving frequency stability in a self-sustained oscillator using a secondary feedback loop acting outside the main sustaining loop with adaptively controlled amplitude and phase shift. We show that quite simple adaptive control laws for the control states of the secondary feedback signal can affect the way that noise circulates in the oscillator so as to reduce or even eliminate (in theory) phase diffusion, i.e., the rate of linear growth with time of the variance of the oscillator output phase. Our rigorous treatment of this effect is based on a linearized analysis of the noisy slow-flow amplitude and phase equations for a relevant general class of systems, from which we derive an explicit expression for the corresponding asymptotic rate of phase diffusion. Using this result, we consider different choices for tailoring the actuation from the secondary feedback loop. This includes tuning a phase coupling constant that ensures that the noise driving the output phase also drives one of the adaptive control states, which represents a generalization of a desirable behavior observed in internally resonant coupled mode operation. We show that such feedback design may be used to eliminate effects that commonly arise from the conversion of amplitude fluctuations to phase diffusion, analogous to operation at zero dispersion points, and to improve upon the phase-cleaning effect associated with internal resonance to even achieve the ideal situation of zero phase diffusion. We validate our theoretical findings with numerical simulations that agree remarkably well with the theory. The presented results establish a framework for achieving extraordinary frequency stability using slowly varying states of the resonator and feedback.