The double-free-layer magnetic tunnel junction has emerged as a promising structure for high-density memory applications due to its enhanced thermal stability. However, optimizing its multiple design parameters through experimental trial and error is prohibitively time-consuming, while existing numerical tools remain inadequate for solving the coupled transport-magnetization dynamics problem. To achieve accurate and efficient optimization, an integrated coupled model combining the transport process and magnetodynamics is proposed, achieving a validated speed-up factor of 117.1 and enabling device-level parameter optimization, such as ferromagnetic coupling strength and thermal stability. The transport process is modeled based on nonequilibrium Green’s function method, accelerated by two numerical techniques: (a) a mixed sampling strategy for energy integrals involving sharp peak-shaped function and (b) a complex integral approach to bypass singularities in energy-resolved functions. For magnetization dynamics, the variable-step 1(2)-order Runge-Kutta method is employed to compute the time evolution of the free-layer magnetizations. With the coupled model, the design space is systematically explored using both single-objective optimization and Bayesian optimization. A minimum switching time of 0.846 ns is achieved at the optimal parameter setting.

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.

Efficient sound absorption at low frequencies remains a major challenge, as most architectured materials rely on resonant mechanisms that limit their bandwidth. Here, we demonstrate a nonresonant approach based on resistive materials coupled to soft acoustic boundaries and show its effectiveness in different setups. In a reflection configuration, a critically coupled wire-mesh layer combined with a foam yields a high and broadband absorption over the investigated frequency range 10–6400 Hz. When considering a transmission configuration, placing a side hole near the wire-mesh layer breaks the mirror symmetry and results in a low-frequency and broadband one-sided absorption. This work opens new opportunities for acoustic materials by leveraging the potential of resistive materials for low-frequency and broadband wave manipulation.

Neutrino detection for reactor monitoring using scintillator (NEREUS) is a proposed mobile freight container-style neutrino detector for reactor monitoring. The detection of unshieldable reactor antineutrinos is a unique probe for reactor monitoring due to its noncontact, continuous, and near-real-time capability. It is an attractive safeguard method for nonproliferation applications and complements current monitoring methods, especially for advanced reactors with high-temperature and high-radiation environments, where traditional methods are difficult to apply. In this work, the design of the NEREUS detector is studied and optimized based on extensive Monte Carlo simulations. Its capabilities for reactor monitoring are assessed, alongside its physics potential for applications including sterile neutrino searches.

We demonstrate a minimally destructive in situ technique for measuring and stabilizing slowly drifting magnetic fields in ultracold-atom experiments. While conventional magnetic-field sensors such as Hall, giant magnetoresistive, or fluxgate-based devices are broadly used, their accuracy, precision, and dynamic range can be limited. In addition, these sensors are typically positioned at least several centimeters away from the in-vacuum atomic system, as their operation creates perturbing magnetic fields, and their placement is limited by geometric constraints imposed by the vacuum system. We overcome these issues by using the atomic system itself as a built-in magnetometer. To that end, we employ a pair of weak measurements to determine the Zeeman splitting—and thereby the magnetic field—of a magnetically sensitive atomic transition. We provide closed-form expressions quantifying the trade-offs among measurement noise, dynamic range, and atom loss. This procedure is demonstrated with ultracold ${}^{87}\mathrm{Rb}$, weakly measured using partial-transfer absorption imaging. We then incorporate a Kalman filter to stabilize the magnetic field; this eliminated long-term drift in the ambient field (as high as approximately 70 nT/hr) in exchange for a modest increase in shot-to-shot variability from 1.8(2) to 2.0(2) nT.

The conventional impedance matching based on Fabry-Perot resonance has been widely used to overcome impedance mismatch. However, the phase of transmitted wave in impedance matching system is fixed to certain value. This constraint in phase limits the applications of wave manipulation, which requires spatial phase distribution. In this paper, we find out that Willis metamaterials composed of geometrically asymmetric unit cell can achieve impedance matching with tunable phase over a geometry-dependent range. By exploiting the complex-valued impedance arising from Willis coupling, we fundamentally alter the classical Fabry-Perot resonance conditions to achieve full transmission. This mechanism enables independent control of the phase of transmitted waves. We validate these theoretical predictions through numerical simulations and experimental demonstrations with continuum aluminum structures. Our findings establish a new platform using Willis metamaterials for versatile wave manipulation devices, overcoming long-standing design constraints.

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.

Nonlinear phenomena play a crucial role across diverse physical systems, providing key insights into complex interactions and emergent collective behaviors. Nonlinear dynamics has been studied around phase transitions, which benefits from the reduced threshold of the intrinsic dynamical instability. Here, we report the experimental observation of magnon mode hopping near the second-order phase transition in yttrium iron garnet film. It is found that a cusplike soft mode exhibits mode hopping under strong driving. The experimental observations are numerically understood from the sharp decrease of the magnetization precession amplitude. The mode hopping is attributed to driving-induced dynamical instability near the phase transition. Our findings could pave the way for investigating nonlinear magnetization dynamics near the phase transition.

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.

The birefringence mode-locked fiber laser-based dual-comb sources have strong potential in the fast spectroscopy, ranging and imaging, due to its large repetition rate difference, configuration compactness, and common-mode noise suppression. However, the intrinsic effects of the mutual dynamics between the dual-polarization solitons (DPSs) output from the dual-comb source on the coherence and common-mode noise still have attracted few attentions. In this work, the polarization mode competition dynamics and the statistical correlation of the intensity jitter between the DPSs are studied, and the short-time common-mode noise of the dual-comb source is revealed. The noise floor of relative timing jitter is as low as $0.01\mathrm{fs}/{\mathrm{Hz}}^{1/2}$, and the integrated jitter from 200 kHz to 96 MHz reaches only 71 fs. The mutual dynamics shows that asynchronous sampling between DPSs can obtain effective time-domain interferometry signals. Eventually, the acoustical vibration signal carried by the sub-fs level relative timing jitter can be measured by calculating the phase of the interference pattern on the shot-to-shot spectrum with an update frame of up to the kHz level. This work provides a comprehensive understanding of the DPSs mutual dynamics and opens the avenue for its application in the time-resolved spectroscopy-based precise measurements.

This work proposes a nonlocal terahertz metasurface that suppresses the gain degradation intrinsic to local-metasurface-aided leaky-wave antennas by maintaining a stable scattering response as the angle at which the guided mode impinges on the metasurface varies with the scanning frequency. This angular insensitivity is realized by engineering the spatial dispersion (i.e., nonlocality) provided by four surface susceptibilities: tangential electric and magnetic, normal magnetic, and magnetoelectric coupling susceptibilities. We analyze the theoretical degrees of freedom available for angular-response control on the basis of these four susceptibilities within a reciprocal, lossless, dipolar framework in an asymmetric environment. On the basis of this analysis, an optimization-based synthesis method is applied to obtain the required susceptibilities, which yield a close approximation of the desired angle-independent scattering behavior. Additionally, an iterative truncation method is introduced to bridge the gap between abstract susceptibility design and physically realizable structures. Full-wave simulations demonstrate a beam-scanning range of nearly 50° across the 1.9–2.6 THz band, representing an approximately 2.6-fold expansion in continuous, well-formed scanning coverage compared with a local-metasurface counterpart designed under otherwise identical conditions.

We report the electrical detection of the spin resonance with temporal resolution in a two-dimensional electron system. Spin resonance was excited by pulses of subterahertz radiation characterized by relatively short rise and fall times on the order of 10 ns and was observed as a resonant peak in the high-frequency conductivity of the two-dimensional channel. The suggested approach is inherently broadband and enables the investigation of spin dynamics in two-dimensional electron systems across a wide range of magnetic fields and temperatures. The proposed technique was employed to measure spin relaxation in a Si/SiGe quantum well.

Background: The formation and spatial distribution of $\mu$-hotspots in high-energy photon radiotherapy are important factors influencing radiation-induced DNA damage. These hotspots can enhance the local linear energy transfer (LET) component, affecting therapeutic outcomes. Purpose: This study aims to investigate the characteristics of $\mu$-hotspots using measurements of the inclusive $\alpha$-particle spectrum from the ${}^{12}\mathrm{C}(\gamma ,3\alpha )$ breakup reaction, induced by 14.8 MeV bremsstrahlung photons, and to correlate these features with radiobiological effects, such as DNA double-strand breaks (DSBs) and micronuclei formation. Results: Geant4 simulations incorporating TALYS-based differential cross sections and angular distributions calculated via distorted wave Born approximation (DWBA), fitted with experimental measurement, reveal that $\mu$-hotspots have a characteristic root-mean-square diameter of approximately $12\mathrm{\mu }\mathrm{m}$ and a mean spatial separation of $120.73\pm 0.0075\mathrm{\mu }\mathrm{m}$ within a $1{\mathrm{cm}}^3$ volume, assuming uniform distribution. The enhancement in DSBs induced by $\mu$-hotspots shows a strong linear correlation with micronuclei formation, indicating a potential mechanistic link. Conclusion: The microscale clustering effect of $\mu$-hotspots significantly increases the high-LET component of the radiation field, with critical implications for radiobiological effectiveness and therapeutic outcomes in photon-based cancer treatments.

We demonstrate a practical and sensitive low-field approach to magnetic resonance imaging (MRI) of $\mathrm{\mu }\mathrm{L}$-scale samples using an optically pumped magnetometer (OPM). Using a simple setup where a commercial OPM is placed directly adjacent to a sample, without intermediary signal-pickup coils, we perform one- and two-dimensional imaging with a $>1\mathrm{cm}$ field of view and sub-mm Fourier-limited resolution at $10\mathrm{\mu }\mathrm{T}$, near Earth’s field. This enables (i) high-throughput, low-field MRI of fluidic and tissue samples, and (ii) a quantitative benchmark of the sensitive volume surrounding the OPM.

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.