Previous theoretical studies have shown that attosecond electron dynamics can, in principle, be captured in electron momentum spectroscopy (EMS) using ultrashort electron pulses. By including further analytical considerations on the scattering probability, we here study the effect of the finite transversal extend of the projectile electron wave packet. We find that in wave packet scattering, the target momentum distribution is probed solely in a finite spatial region. This is evident from a spatially filtering Gabor transform appearing in the scattering probability, replacing the full momentum wave function appearing in the conventional plane wave treatment. In addition, by spatially shifting the target with regard to the wave packet focus, we illustrate the influence of vacuum dispersion, i.e., the spatial broadening of the wave packet as it propagates. Our findings are significant for the possibility to correctly interpret future attosecond-EMS results and the considered effects reflect fundamental aspects of wave packet scattering. The EMS setup may, therefore, constitute a useful framework for understanding scattering with finite wave packets.

We present a method for the numerical analysis of superconducting quantum circuits combining lumped elements, either linear or nonlinear (i.e., Josephson junctions), and distributed coplanar waveguide (CPW) structures. CPW transmission lines and multiline couplers are directly modeled without discretizing them into lumped-element equivalents, and the circuit Hamiltonian parameters are extracted by using the energy-participation ratio (EPR) method. This approach enables fast and accurate extraction of mode frequencies, anharmonicities, cross-Kerr interactions, and Purcell decay rates without relying on full electromagnetic simulations, while naturally accounting for higher-order modes of distributed components. We have implemented the proposed method in a Python framework, QuLTRA (Quantum hybrid Lumped and TRansmission lines circuits Analyzer), which we have used to validate the approach against full electromagnetic simulations (Ansys HFSS, pyEPR), existing circuit-analysis tools (QuCAT), and designs reported in the literature. The comparisons show excellent agreement with orders-of-magnitude reductions in computational time relative to full-wave solvers. We demonstrate applications including Purcell-protected readout, multimode ultrastrong coupling, and multiplexed qubit readout, illustrating how the method can support fast and reliable early-stage circuit design.

We numerically study confined channel flow around an obstacle using a two-dimensional soft-particle model, inspired by experiments performed in the same geometry. We systematically vary the polydispersity, the external driving force, and the packing fraction of the system. Our simulations capture a broad range of plastic flow phenomenologies, from highly directional, slidinglike motion characteristic of crystalline materials to more isotropic and localized rearrangements typical of amorphous systems. We identify a threshold value of polydispersity that marks the crossover between crystallinelike and amorphouslike plasticity. In addition, we observe the existence of a critical external force, associated with the phenomenon of yield drag, above which the system reaches steady flow and below which it remains arrested. We determine a critical packing fraction above which such yield-drag behavior emerges. Our results provide a comprehensive framework for understanding the interplay between disorder, driving, and the presence of an obstacle in channel flows.

Finite-time thermodynamic transformations generally dissipate energy, as required by the second law, so identifying and investigating energetically optimal processes remain relevant for understanding and designing efficient thermal devices. In this context, we present here a systematic experimental benchmark of the finite-time minimum-work compression protocol derived by Schmiedl and Seifert for an overdamped Brownian particle in a harmonic trap. By parametrically controlling the trap stiffness, we compare the full work statistics of the analytical optimal protocol with those obtained under linear driving across a broad range of protocol durations and amplitudes. We find that the optimal protocol consistently reduces both the mean work and the work variance relative to linear driving. Although the Schmiedl-Seifert protocol was primarily derived to minimize the mean work, our data show, in the present setting, a clear reduction in fluctuations and high-work tails under realistic bandwidth-limited actuation. This observation connects our benchmark to the growing interest in higher-order cumulants and to current developments in stochastic-thermodynamic control.

The gradient boosting regressor, an ensemble learning technique, is utilized to correct the residuals in nuclear mass excess across six different nuclear mass models. The corrected residuals are substantially reduced and further improved by applying a weighted averaging method, which benefits from partial error cancellation among the models. Our proposed conflated model, combining ensemble learning with model averaging (ELMA), achieves a root mean square error (RMSE) of approximately 57 keV, well below the critical threshold of 100 keV, for the full dataset of the Atomic Mass Evaluation (AME2020). ELMA also improves predictions for nuclei beyond the training region, with the weighted ensemble reducing the RMSE by approximately 25%–45% relative to the best individual mass model on independent extrapolative datasets. The reliability of the ELMA approach is further validated through the calculation of $\alpha$-decay $Q$ values, where a clear reduction in deviations from experimental data is observed compared to individual nuclear mass model predictions. We have also compiled a mass excess and binding energy of 6300 nuclei, obtained using our framework and serves as a valuable resource for nuclear physics research and is publicly available through the ELMA web interface (https://ddnp.in/).

High-dimensional biomedical studies routinely confront three coupled obstacles: structured population heterogeneity, latent technical confounding, and representational inequity that can degrade inference for under-represented groups. We develop the model-based equitable learning framework (M-ELF), a probabilistic inference architecture that unifies (1) a hierarchical mixture of factor analyzers for subgroup-aware latent structure learning, (2) a negative-control-informed adjustment that removes latent confounding through a partitioned Gaussian conditioning argument, and (3) a spectral-entropy equity regularizer that discourages representation collapse within minority subpopulations using a normalized Vendi score. The M-ELF formulation makes explicit the assumptions required for valid deconfounding and for cross-population comparability of latent factors. The model yields a scalable variational-EM procedure that couples conjugate mixture-factor updates with differentiable spectral regularization. We establish conditions for generic identifiability (up to standard mixture label switching and random-effect rotations), derive exact confounder-elimination statements for the conditional model, and provide an ascent and stationarity analysis for the regularized algorithm. Simulations and a real breast-cancer gene-expression application illustrate that, relative to unadjusted analysis, M-ELF attenuates associations with technical confounders by up to $\sim 86\%$ while preserving factor recovery under equity regularization. In the TCGA breast-cancer data, M-ELF attained higher PAM50 subtype agreement (adjusted Rand index) and lower batch association than the specialized deconfounders RUV-2 and PEER, with the equity weight $\gamma$ mildly trading off subtype agreement against representation balance. In the model-based simulations, specialized methods can achieve higher factor recovery on individual metrics, and the equity penalty produces only modest changes in cross-population diversity. M-ELF's advantage is the unified probabilistic treatment of subgroup structure, negative-control deconfounding, and equity-aware representation, and $\gamma$ should be selected against the scientific end point.

Rydberg sensors offer a unique approach to radio-frequency (rf) detection, leveraging the high sensitivity and quantum properties of highly excited atomic states to achieve performance levels beyond classical technologies. Nonlinear responses and distortion behavior in Rydberg atom receivers are critical to evaluating and establishing performance metrics and capabilities such as spur-free dynamic range and tolerance to unwanted interfering signals. We report here on the measurement and characterization of nonlinear behavior and spurious response of a Rydberg atomic heterodyne receiver. Single-tone and two-tone testing procedures are developed and implemented for measurement of harmonic and intermodulation distortion in Rydberg atomic receivers based on multiphoton Rydberg spectroscopy and radio-frequency heterodyne signal detection and demodulation in an atomic vapor. For a predetermined set of atomic receiver parameters and rf carrier wave in the super high frequency band near-resonant to a cesium Rydberg transition, we measure and characterize atomic receiver selectivity, bandwidth, roll-off, compression point (P1dB), second-order (IP2) and third-order (IP3) intercepts, and spur-free dynamic range. Receiver intermodulation distortion is characterized for the case of an interfering signal wave applied at two frequency offsets relative to the near-resonant reference local oscillator, $\mathrm{\Delta }F/F={10}^{-4}$ at 6 dB and ${10}^{-6}$ at 22 dB single-tone bandwidths, respectively. We observe that under suitable operating conditions the atomic receiver can exhibit a suppression of harmonic and intermodulation distortion relative to that of classical receiver mixer amplifiers.

The non-Hermitian spinful Aubry-André-Harper model in the presence of Rashba-type spin-orbit coupling and a spatially varying textured magnetic field is studied. Interestingly, our analysis produces a butterfly spectral map due to the nontrivial extent of localization of the states in the spectrum. This spectral map also exhibits an asymmetric spin alignment with respect to the wings of the butterfly. Our analysis also suggests that the onset of such a spectral map is a combined effect of the non-Hermiticity, spin-orbit interaction, and the textured magnetic field.

Continuous far-field zone focusing of flexural wave is highly desirable for applications such as structural health monitoring, energy harvesting, and advanced sensing. In this work, we propose a metalens design paradigm capable of focusing flexural wave across a continuous zone in the far-field region. The metalens is based on an intelligently designed metagrating, in which each unit cell consists of two elliptical holes etched through a thin aluminum plate. Each unit is designed to deflect the incoming waves along a prescribed direction with tunable reflection phase and near-unitary reflection efficiency. The metagrating is engineered such that different unit cells direct the incident wave toward distinct subregions within the target focusing zone. When the reflection phase is uniformly maintained across the metagrating, a continuous extended focusing zone is formed through the superposition of wave beams reflected from all unit cells. This approach achieves a focusing area of approximately $2900{\lambda }^2$ in the far field, with an average intensity enhancement factor of 9.0 and an almost homogeneous intensity distribution. The compact planar architecture of the metalens not only offers a flexible and practical solution for far-field zone focusing, but also holds promise as a versatile planar component suitable for system integration.

Terahertz (THz) spectroscopy holds transformative potential for noninvasive sensing, yet characterizing individual nanoparticles in complex biological environments remains challenging due to the far-field diffraction limit. While near-field dipolar theory is well established, its application to characterizing/identifying nanoparticles immersed in complex media at THz frequencies is largely unexplored. This work utilizes numerical simulations of magneto-optical (MO) heterodimers—comprising $n$-doped indium antimonide ($\left[n\text{-InSb,}\text{GaSe}\right]$) and isotropic or birefringent particles (e.g., ${\text{SiO}}_2, \text{GaSe}$)—under counterpropagating, circularly polarized THz illumination. The analytical framework is supported by a numerical validation against full-wave simulations. We demonstrate that while far-field observables like absorption cross sections are often dominated by the MO-active particle, mechanical variables—specifically induced binding forces and spin/orbital torques—exhibit superior sensitivity for detecting “hidden” neighboring components. Because these mechanical signatures depend directly on near-field interactions, they provide higher information density regarding interparticle coupling. Key findings reveal material-specific spectral “hot spots” and “zeros” that serve as robust calibration markers even within dispersive biological surrogates. We show that the spin torque on non-MO particles is significantly modified by MO-neighbor proximity, a phenomenon controllable via static magnetic fields. While experimental implementation in complex media presents significant challenges, our results establish a theoretical roadmap for using optomechanical signatures as high-resolution detection channels for diagnostics, signal transduction, and low-energy nanocircuit control.

Axions are hypothetical particles that could address both the strong charge-parity problem in quantum chromodynamics and the enigmatic nature of dark matter. However, if axions exist, their mass remains unknown, and they are expected to interact very weakly with the electromagnetic field, which explains why they have not been detected yet. This study proposes a way to substantially augment the axion-photon interaction by confining the photons within high-quality-factor dielectric resonators, increasing their intensity and lifetime, and thus the possibility of interacting with axions in the background. In view of this, we study resonant axion-mediated photonic transitions in millimeter-sized spherical dielectric resonators, based on fully analytical calculations to the first order in perturbation theory. Such resonators exhibit high-lifetime Mie resonances in the microwave part of the spectrum, with a separation that can be tailored with the radius of the sphere to match the expected axion frequency, allowing axion-mediated photonic transitions when particular selection rules are fulfilled. We predict experimentally accessible axion mass regimes where such triply resonant transitions can be realized with standard dielectric resonators. We propose an experiment for probing such interactions named DARK-ROSE.

The theoretical understanding of conventional superconductivity as the phonon-assisted formation and condensation of two-electron Cooper pairs is a significant triumph in condensed matter physics. Here, we propose many-electron characterizations of higher-charge superconductivity with Cooper pairs consisting of more than two electrons, by implementing translation symmetrization on parent pair-density-wave-ordered states. In particular, we demonstrate many-electron constructions with vanishing charge-$2e$ sectors, but with sharp signatures in charge-$4e$ or charge-$6e$ expectation values instead. Such characterizations are consistent with previous phenomenology of vestigial order and Ginzburg-Landau theory. Furthermore, we demonstrate that at the microscopic level, momentum conservation alone may be vital and sufficient for the robust emergence of higher-charge superconductivity. Our study thus offers a general and microscopic route to understand and characterize higher-charge superconductivity, including nontrivial experimental signatures such as fractional magnetic flux and period in interferometry, as well as localized Cooper pairs at lattice topological defects.

A classical-limit formula is derived for matrix elements between bound states of distinct one-dimensional potentials. We leave open the physical interpretation of the potentials, but generally envision them to be Born-Oppenheimer-type potentials with the position variable $x$ identifying with a slowly evolving degree of freedom of the system. For instance, $x$ could represent the internuclear separation in a diatomic molecule, with the potentials being potential energy curves for different electronic states. In this scenario, the matrix elements could be, e.g., conventional Franck-Condon factors. To test the derived formula, we assume functional forms for the potentials and operator that afford analytical solutions for the matrix elements. As the classical limit is approached, the computed matrix elements exhibit a clear tendency toward the classical-limit formula, providing strong validation for the formula. In future work, we anticipate using the formula to model inhomogeneous excitation spectra of atoms in one-dimensional optical lattices, with an eye toward improved optical lattice clock performance.

Topological quantum computing promises intrinsic fault tolerance by encoding quantum information in non-Abelian anyons, where quantum gates are implemented via braiding. While braiding operations are robust against local perturbations, a critical yet often overlooked challenge arises when scaling beyond two qubits: the naive extension of braiding-based gates fails to support even the full Clifford group. To overcome this limitation, we incorporate projective measurements that enable transitions between different qubit encodings, thus restoring computational universality. We perform many-body simulations of braiding dynamics augmented with measurement-based switching, explicitly preparing the Bell state and Greenberger–Horne–Zeilinger state for systems of two and five qubits, respectively. Furthermore, we execute a random unitary circuit on five qubits, achieving a fidelity exceeding 99%. We analyze the circuit's robustness by studying its fidelity dependence on total braid duration and static potential disorder. Our results show that the fidelity remains above 99% for moderate disorder, underscoring the intrinsic fault tolerance of the architecture. Finally, we demonstrate a random circuit on a ten-qubit system to showcase the scalability of our techniques.

Photon-number-resolved detection with superconducting nanowire single-photon detectors (SNSPDs) attracts increasing interest, often focusing on the rising edge of the electrical signal as the main source of photon-number-resolving information. It remains unclear whether additional photon-number information is contained in the rest of the detector response and how this can be optimally extracted and interpreted. In this work, we combine principal component analysis (PCA) with readout at relatively low sampling rates to explore the photon-number-resolving capabilities of SNSPDs. We find that the information of the photon number is captured by a single principal component that closely resembles the time derivative of the average response trace. Using the time derivative of the average response trace provides an agnostic method to obtain the photon number from SNSPD traces without the necessity of performing PCA, making this method simple and quick to calibrate. As an auxiliary diagnostic, we use a distinguishability metric based on the Bhattacharyya coefficient [Indian J. Statistics 7, 401 (1946)] to quantify and compare the photon-number-resolving capabilities of different detector systems. Our analysis and interpretation of the principal components imply that photon-number resolution in SNSPDs can be achieved with moderate hardware requirements in terms of both sample rate (5 GS ${\mathrm{s}}^{-1}$) and analog bandwidth (3 GHz) and could be implemented in a field-programmable gate array, giving a highly scalable solution for real-time photon counting.

We develop a one-class, deep-learning framework to detect the phase transition and recover critical behavior of the three-dimensional (3D) Ising model. A 3D convolutional autoencoder (CAE) is trained on ground-state configurations only, without prior knowledge of the critical temperature, the Hamiltonian, or the order parameter. After training, the model is applied to Monte Carlo configurations across a wide temperature range and different lattice sizes. The mean-square reconstruction error is shown to be sensitive to the transition. Finite-size scaling of the peak location for the reconstruction error susceptibility yields the critical temperature $T_c=4.5128\left(58\right)$, in excellent agreement with the known value. Additionally, we obtain an estimate of the correlation-length critical exponent, $\nu =0.63\left(27\right)$, also consistent with results from the literature. Our results show that a one-class CAE, trained on zero-temperature configurations only, can recover nontrivial critical behavior of the 3D Ising model.

A metric is called universal if every symmetric conserved rank-two tensor constructed locally from the metric, the curvature, and covariant derivatives of the curvature is proportional to the metric. Such a metric solves arbitrary metric-based higher-curvature field equations after, at most, a change in the effective cosmological constant. A metric is called almost universal if the same field equations reduce instead to a finite set of algebraic conditions and a single linear scalar equation for a profile function. Universal and almost universal metrics are useful because they keep higher-curvature gravity under analytic control. Plane waves are a well-known example of universal metrics, while the Kerr-Schild-Kundt class of metrics including the $pp$ waves is almost universal. Here, we add a member to this class of metrics and show that nonzero constant curvature $pp$-wave metrics are also almost universal. They reduce the generic gravity field equations to those of cosmological Einstein-Maxwell theory with null dust. The background of the $pp$ waves has the topology ${\mathbb{R}}^{1,1}\times S^2$ and provides the missing partner to the Nariai metric with ${\mathrm{dS}}^2\times S^2$ and the Bertotti-Robinson metric with ${\mathrm{AdS}}^2\times S^2$ topologies. These quantum-protected metrics are of clear interest. We exemplify our results by using the quadratic and cubic gravity theories.

We study the spectral statistics and wave-function properties of a one-dimensional quantum system subject to a Cantor-type fractal potential. By analyzing the nearest-neighbor level spacings, inverse participation ratio (IPR), and the scaling behavior of the integrated density of states (IDS), we demonstrate how the self-similar geometry of the potential is imprinted on the quantum spectrum. The energy-resolved level spacings form a hierarchical, filamentary structure, in sharp contrast to those of periodic and random systems. The normalized level-spacing distribution exhibits a bimodal structure, reflecting the deterministic recurrence of spectral gaps. A multifractal analysis of eigenstates reveals critical behavior: The generalized fractal dimensions $D_q$ lie strictly between the limits of extended and localized states, exhibiting a distinct $q$ dependence. Consistently, the IPR indicates the coexistence of quasiextended and localized features, characteristic of critical wave functions. The IDS shows anomalous power-law scaling at low energies, with an exponent close to the Hausdorff dimension of the underlying Cantor set, indicating that the geometric fractality governs the spectral dimensionality. At higher energies, this scaling crosses over to the semiclassical Weyl law. Our results establish a direct connection between deterministic fractal geometry, hierarchical spectral statistics, and quantum criticality.