Papers

Optical frequency combs are key components of several photonics applications including spectroscopy, communications, and ultrafast photonics. A central challenge in frequency-comb photonics is to develop sources whose operating state can be precisely controlled and adapted to different application needs. We introduce frequency comb functionality to a THz metasurface vertical-external-cavity-surface-emitting laser (VECSEL), combining its characteristic high output power and excellent beam quality with a reconfigurable comb output. The source exhibits reversible switching between actively mode-locked 3.5 ps-long pulses and stable frequency-modulated quantum walk comb states. The flexible control of the intermodal phase relation is achieved through careful dispersion engineering via a Gires-Tournois interferometer (GTI) output-coupler combined with resonant RF bias modulation of the metasurface. These results pave the way for on-demand comb control in the THz range and provide a versatile strategy that could be extended to other semiconductor frequency-comb platforms and wavelength ranges.

We investigate the impact of new physics on the rare inclusive decay $B \to X_{\mathrm{s}} l^{+} l^{-}$ within the framework of the $\mu$ from $\nu$ Supersymmetric Standard Model ($\mu\nu$SSM). The dominant contributions to the relevant Wilson coefficients and their corresponding particles are identified and analyzed. By performing a systematic scan over the relevant parameter space, we elucidate the underlying physical mechanisms governing these dominant contributions and demonstrate their consistency with the experimentally allowed regions. Experimental constraints from the decays $\bar{B} \to X_{\mathrm{s}}\gamma$, $B_{\mathrm{s}}^{0} \to \mu^{+} \mu^{-}$, and the $125\,\text{GeV}$ SM-like Higgs boson are also incorporated. We perform a systematic interference decomposition of the Wilson-coefficient contributions to the forward-backward asymmetry, identifying the $C_7C_{10}$ and $C_9C_{10}$ interference terms as the dominant contributions governing its behavior in both the low- and high-$q^2$ regions.

The DFT-D3 dispersion correction is routinely added to machine learning force fields (MLFFs) trained on dispersion-deficient functionals such as PBE. Its environment-dependent pair coefficients, however, break the atom-centered separability that fast summation methods require, forcing practitioners either to truncate D3 or to accept a substantial slowdown. We introduce FourierD3, a method that uses a functional low-rank decomposition to restore this separability and enable particle-mesh evaluation in $O(N\log N)$ time without a real-space cutoff on the dispersion sum.

The kinetics of protein-ligand binding systems are increasingly recognized as a key determinant of drug efficacy, yet remain far harder to compute than binding affinities. Existing kinetics methods either bias the dynamics along a collective variable (CV), demanding careful system-specific CV design, or use path sampling, which keeps the dynamics unbiased but can struggle to converge rates out of deep free-energy wells and often relies on hand-engineered descriptors. By combining the `best of both worlds', we propose a method to compute accurate kinetics for general ligand-unbinding problems at modest computational expense and minimal fine tuning, building on the AI for Molecular Mechanism Discovery (AIMMD) path sampling framework. To avoid the need for feature engineering, we opt for modelling the committor with a single descriptor-free, equivariant graph neural network shared across all systems. We also partially flatten deep bound-state wells with a static, basin-restricted bias potential. This improves convergence by lifting the path sampling state boundary out of regions, where the committor is hard to learn, while leaving the reactive region strictly unbiased. Across host-guest and protein-ligand systems spanning roughly 17 orders of magnitude in residence time, the method robustly recovers rates in line with reference and experimental values. Simultaneously, and without further sampling, it also reconstructs the underlying unbinding mechanisms. We additionally find that accurate rates do not require globally accurate committor models, allowing for efficient kinetics estimation even in a low-data training regime. Requiring little system-specific setup, our approach offers an efficient and broadly generalizable route to binding kinetics, and its shared committor architecture lays crucial groundwork for probing structure-kinetics relationships across ligand series in drug discovery.

Earth attenuation is crucial for interpreting direct-detection constraints on boosted dark matter (DM), since scatterings with terrestrial nuclei can significantly modify the flux and energy spectrum reaching underground detectors. At boosted energies, inelastic nuclear channels beyond ordinary elastic scattering can become relevant, including quasi-elastic scattering, deep-inelastic scattering, and resonant scattering. In this work, we incorporate the resonant scattering of boosted dark matter (DM) off nuclei into the Earth-attenuation framework, in combination with the elastic, quasi-elastic, and deep-inelastic channels. We find that, in the heavy-mediator regime, resonant scattering can give a non-negligible contribution to the attenuation of boosted DM. Using the latest PandaX-4T data, we derive new constraints on the spin-independent boosted DM-nucleon cross section $\bar{\sigma}_n$.

Magnetic monopoles lie at the crossroads of gauge fields, topology, geometric phases, and charge quantization, yet they remain elusive as fundamental particles. Here we show that an emergent Dirac-monopole framework arises naturally from the dynamics of massive quantum vortices in a spherical superfluid shell. Their dynamics is formally equivalent to that of interacting charged particles constrained to a sphere in the field of a magnetic monopole. The monopole charge is fixed by the superfluid density and automatically satisfies Dirac's quantization condition. The emergent monopole description predicts cyclotron-like vortex motion, in quantitative agreement with Gross–Pitaevskii simulations. We further show that topological frustration induced by two like-charged polar vortices gives rise to the formation of an equatorial vortex necklace, a configuration reminiscent of the polygonal cyclone clusters observed around Jupiter's poles, before its subsequent breakup through a Kelvin–Helmholtz-like instability. Within this framework, the equatorial vortex necklace emerges as the quantized realization of the effective electrostatic line charge that restores global charge neutrality on the sphere. Our results establish spherical superfluids as a versatile platform for realizing and exploring fundamental aspects of Dirac-monopole physics.

We employ a mathematically equivalent form of the GKLS master equation to arrive at an exact theoretical description of a two-level system strongly coupled to the environment. The framework, while intuitive, shedding light on the physics of the problem, and agreeing with existing results, such as thermalisation to a non-canonical state, is based around three parameters that are unknown outside of the weak coupling regime – the analogue to the detailed balance relation, and two coupling strength constants. As a way forward, we propose a feasible experimental protocol based on a solid-state electronic quantum dot device, through which the fundamental parameters of the problem can be revealed, which would further the fundamental understanding of strong coupling.

Large Faraday rotations can be generated by circular birefringence of atomic samples in an axial magnetic field in the vicinity of atomic resonance lines. The Faraday angle is a function of the magnetic field strength, the optical density of the atomic sample which may be varied by changing the temperature of the atomic gas, and of course the optical detuning from the transition frequencies. More generally, magneto-optical effects in atomic samples include circular dichroism in addition to birefringence, resulting in a modification of the ellipticity as well as the polarisation alignment. Usually such effects are investigated for homogeneous linear polarisations, but the mechanisms apply also to polarisation structures such as vector vortices. We investigate the effect of optical activity of a rubidium vapour in the Hyperfine Paschen-Back regime, for the example of an azimuthally polarised input light beam. We show that for low atomic densities, circular birefringence dominates over dichroism, and azimuthal polarisation is rotated towards radial polarisation. The rotation angle increases with increasing optical densities. At high vapour temperatures, dichroism becomes more and more relevant, leading to intricate variations of both alignment and ellipticity.

Physics-informed neural networks (PINNs) can solve forward and inverse PDE problems but may struggle with spectral bias and oscillatory, high-frequency, or phase-dependent solutions. We introduce a generalized split complex-valued PINN (SCV-PINN) framework with complex-valued parameters and latent representations. Split activations act separately on real and imaginary components while complex operations preserve their coupling, enabling amplitude–phase representations with practical optimization. Ablation studies evaluate split activation functions and collocation sampling strategies. Forward and inverse benchmarks include the Burgers, Allen–Cahn, Korteweg–de Vries, nonlinear Schrödinger, Helmholtz, and Poisson equations on regular and irregular domains; Kovasznay flow ($\mathrm{Re}=20$); lid-driven cavity flow ($\mathrm{Re}=100$); the Lorenz system; and inverse Burgers and Navier–Stokes problems ($\mathrm{Re}=100$). A three-dimensional Navier–Stokes Beltrami flow further demonstrates applicability to higher-dimensional nonlinear dynamics, with a relative $L_2$ error of $4.07\times10^{-5}$. Across these tests, SCV-PINN yields high-fidelity solutions and effective parameter identification, improving on real-valued PINNs and selected variants. The results suggest that split complex-valued representations can help address spectral-bias-related difficulties in nonlinear and oscillatory physical systems.

We study time-discrete parametric approximations of evolution equations in Hilbert spaces based on residual minimization. The solution is represented by a parametrized ansatz belonging to a low-dimensional nonlinear manifold, and time stepping is performed by minimizing suitably defined residuals at each step. Two natural residual formulations are considered: discretization followed by parametrization of the evolution equation, and discretization of the Dirac–Frenkel variational principle governing the parameter dynamics. A unified error analysis is developed for both approaches within the family of $\zeta$-methods. The resulting bounds separate the effects of time discretization from those of residual minimization and yield first- and second-order convergence under Lipschitz, one-sided Lipschitz, and dissipativity assumptions. For the variational formulation, additional stability conditions involving the conditioning of the parametrization map arise naturally. The framework is applied to Gaussian approximation manifolds, for which residual norms and gradients admit explicit closed-form expressions when polynomial operators are involved. This enables efficient implementation without spatial discretization. Numerical experiments for time-dependent Schrödinger equations illustrate the theoretical convergence rates and the influence of residual accuracy on conservation properties.

The coupled dynamics of local fields exert a drastic influence on the light-activated self-propulsion of a Janus particle in a binary solvent under spatial confinement. In this work, we investigate this problem using numerical simulations that account for local phase separation and wetting phenomena, as well as hydrodynamic effects. We find that confining the binary solvent within a channel results in a reduction of the active particle's propulsion speed and an extension of the duration of its directed motion. Furthermore, the orientational dynamics of this self-propelled particle are not restricted to two dimensions, unlike the phenomenon known as "orientational quenching". Increasing the light intensity leads to strong fluctuations in the local fields and, consequently, in the particle's speed. In this context, the significance of key physical parameters governing the efficiency of particle motion control is elucidated.

We propose a generalization of the defect extremal surface (DES) prescription to holographic interface conformal field theories (ICFTs), extending its applicability beyond the AdS$_3$/BCFT$_2$ framework. We consider a holographic ICFT$_2$ comprising two CFT$_2$s coupled across an interface, dual to two asymptotically AdS$_3$ geometries joined by a codimension-one end-of-the-world (EOW) brane. In the corresponding effective lower-dimensional description obtained via partial dimensional reduction on the brane-boundary combination, the theory on the brane is coupled to gravity, thereby enabling the computation of fine-grained entanglement entropy via the island formula. We demonstrate that the generalized DES prescription exactly reproduces the effective lower-dimensional results for finite subsystems in time-independent scenarios in the large brane tension limit, corresponding to weak gravitational coupling on the EOW brane. We further compute the entanglement entropy of semi-infinite bipartite pure states in radiation subsystems coupled to $2d$ eternal black holes, and obtain the corresponding Page curves.

Accurate modeling of compressible premixed flames, encompassing both deflagrations and detonations, remains a significant challenge for predictive Large Eddy Simulation (LES) due to the strong coupling between the thermochemical state and the local thermodynamic state. This work presents a manifold-based turbulent combustion model that ensures a fully consistent thermodynamic state between model and flow solver through an iterative procedure. The framework reproduces critical quantities including temperature, radical species, and source term profiles, addressing limitations of existing approaches that rely on low-Mach perturbations or tabulated ZND detonations without thermodynamic consistency. Validation is performed against one-dimensional and high-fidelity RDE-like data, demonstrating that the thermodynamically consistent model consistently outperforms existing approaches across a broad range of compressible flame regimes - including both deflagration and detonation. The results highlight the importance of fully accounting for the thermodynamic state to achieve accurate predictions. By capturing both deflagrative and detonative behavior within a single framework, the model provides a unified, versatile tool for LES of high-speed reacting flows and offers a foundation for future studies of compressible reacting flows, including applications to rotating detonation engines and other supersonic combustion systems.

Sample-based Quantum Diagonalization (SQD) recovers ground-state energies by classically diagonalizing a Hamiltonian in the subspace spanned by quantum samples, requiring only bitstrings with sufficient ground-state overlap rather than an accurate variational energy. We reveal and exploit this underexplored robustness property: how much non-Clifford and variational expressivity can be removed from the sampling circuit before SQD accuracy degrades? We answer through two complementary compression techniques: gradient-based operator pruning, which discards low-impact excitation operators, and Clifford rounding, which snaps remaining parameters to the nearest Clifford angle. Both of these techniques can be applied to a VQE ansatz on a qubit-reduced Hamiltonian. A systematic ablation study across 21 molecules shows that median SQD error stays within chemical accuracy even at 50% compression on both axes, while simulation speedup reaches $33\times$. Hardware validation on 6 molecules on IBM quantum hardware confirms up to $2.8\times$ transpiled-depth reduction with zero loss in SQD accuracy. Our implementation can be found at: https://github.com/zkysfls/cs-vqe-sqd

Given a compact CY3 $Y$, its A-side (resp. B-side) universal intermediate Jacobian $X$ (resp. $X^{!}$) admits a natural hyperkahler structure. Both $X$ and $X^{!}$ determines 3d Rozansky-Witten theories, in both A-model and B-model. We describe some surprising 3d mirror symmetry phenomena between $X$ and $X^{!}$. This includes (i) a 3d SYZ construction of $X^{!}$ from $X$ via moduli of certain 3d A-branes in $X$ constructed from $D^{b}\left( Y,\Omega \right) $ with varying stability conditions given by $\varpi $; (ii) The 3d B-brane on $X^{!}$, constructed from varying $D^{b}\left( Y,\Omega \right) $ with fixed $\varpi $, is realized as the 3d SYZ transformation of a cotangent fiber 3d A-brane in $X$. Under mirror symmetry between $Y$ and $Y^{\vee }$, the roles for $X$ and $% X^{!}$ got interchanged. We also construct 3d A- and B-branes in $X$ and $X^{!}$ via DT theory and discrete symmetries on $Y$ and $Y^{\vee }$.

Total-f five-dimensional gyrokinetic simulations are essential for self-consistent studies of multiscale, multiphysics transport in the edge region of diverted tokamak plasmas. However, conventional initialization with a local Maxwellian distribution often generates large-amplitude transients, particularly geodesic acoustic modes (GAMs). These transients are especially severe in the plasma edge because of steep profile gradients, strong radial electric fields, and high safety factors, and they can interfere with early-time turbulence and transport diagnostics. To address this problem, we present a new initialization scheme for the total-f XGC code that uses a relaxed particle distribution obtained from a computationally inexpensive axisymmetric simulation. Before the distribution is transferred to the full turbulence simulation, phase-space smoothing is applied to reduce particle noise while preserving its neoclassical structure. Simulations of the Cyclone Base Case and an ASDEX Upgrade I-mode discharge show that the method reduces particle noise and substantially suppresses initialization-driven transients.

We investigate the Casimir effect for a massive real scalar field confined between two perfectly reflecting parallel plates in the presence of a position-dependent effective mass, a mechanism for coupling a scalar background to a scalar field. Exact normal modes are obtained by solving the corresponding Klein-Gordon equation, leading to a transverse energy spectrum that exhibits a characteristic Landau-like structure despite the absence of an external magnetic field. Upon quantization of the field, the vacuum energy is evaluated by means of generalized zeta-function regularization together with an appropriate renormalization procedure. The renormalized vacuum energy naturally separates into a Landau-like contribution and an additional term induced by the spatial dependence of the effective mass. We show analytically and numerically that both contributions are exponentially suppressed in the strong-coupling regime. In the opposite limit, the Landau-like contribution smoothly reproduces the standard vacuum energy for a massive scalar field confined between parallel plates, whereas the additional contribution becomes singular owing to the restricted domain of validity of the exact spectrum. Except in the vicinity of this singular limit, the vacuum energy is shown to be dominated by the Landau-like sector. Our results establish a direct connection between position-dependent effective masses and boundary-induced quantum vacuum phenomena, providing a new exactly solvable framework for the investigation of Casimir effects in spatially inhomogeneous relativistic systems.

Dissociative chemisorption (DC) of H$_2$ on the Cu(111) surface is a prototypical problem for understanding elements of heterogeneous catalysis [Science 326, 832 (2009)]. The challenge lies in modeling the reaction dynamics that in turn reflects a classical potential for atomic deformations, friction, and inelastic scattering. Here, I test the use of a set of range-separated hybrid (RSH) van der Waals density functionals (vdW-DFs) [JPCM 37, 211501 (2025)] on their ability to describe the classical barrier for dynamics in this H$_2$+Cu(111) DC problem. I furthermore document use of a variant for fast accurate predictions of the molecular quasi-particles (QPs), finding excellent performance across a set of small molecules that are often studied in catalysis. Finally, I suggest and implement a way to use that QP focus to identify what I consider a best-possible non-empirical (yet adsorbate specific) RSH vdW-DF version, denoted AHBR($\gamma^*$) for H$_2$ DC modeling, navigating what are partly conflicting requirements on the molecule and metal sides. I find that the AHBR($\gamma^*$) can determine the classical H$_2$+Cu(111) DC barrier height close to chemical accuracy. I suggest that DC modeling can test broader relevance of the physics underpinning these RSH vdW-DFs.

Nuclear binding energies and charge radii stem from the same underlying physics: saturation, isospin dependence, shell structure, and deformation. Binding-energy data therefore provide a natural constraint for charge-radius modeling. We propose a one-parameter charge-radius formula ($\mathrm{BECR}_\mathrm{1p}$) that combines binding-energy correlations with local structural corrections. On a curated set of 893 experimental charge radii, the macroscopic BECR term alone reproduces the leading charge-radius scale with a root-mean-square deviation (RMSD) of 0.0345 fm; adding shell, odd–even, finite-size, and deformation corrections further reduces the RMSD of BECR1p to 0.0138 fm. An anisotropic kernel ridge regression (AKRR) applied to the residuals further lowers the leave-one-out cross-validation RMSD to about 0.0081 fm. We use the formula to predict charge radii for 11205 nuclei across the nuclear chart.

Recent experimental results in high-multiplicity proton-proton (pp) collisions have suggested the possible emergence of collective behavior and medium-like effects previously considered characteristic of heavy-ion collisions. Resonance production provides a sensitive probe of such effects, as resonance yields and transverse-momentum distributions can be modified by hadronic interactions occurring between chemical and kinetic freeze-out. In this study, these effects are investigated using the EPOS4 event generator, in which hadronic final-state interactions are modeled through the UrQMD transport approach. By comparing calculations performed with and without UrQMD, the impact of hadronic interactions on resonance production is evaluated. In addition, the UrQMD contributions are separated into regeneration and rescattering, enabling a detailed investigation of both resonance production enhancement and the loss of reconstructible resonance signals. The analysis is performed for various mesonic and baryonic resonances with different lifetimes in pp collisions at LHC energies and is extended to p-O, O-O, and Pb-Pb collisions to study the system-size dependence of hadronic-phase effects. The results show that resonance production is governed by the competition between regeneration and rescattering, whose relative importance depends strongly on the resonance species, transverse momentum, and collision system. While rescattering suppresses reconstructible short-lived resonance signals, regeneration can significantly enhance the yields of several resonance species, particularly baryonic resonances. These findings demonstrate that hadronic interactions can play an important role even in small collision systems and highlight the need to measure resonances with different lifetimes and quantum numbers to constrain the dynamics and lifetime of the hadronic phase across collision systems.

Starting from a probabilistic numerics approach to the Fermion sign problem in path integral Monte Carlo, we recast the arithmetic calculation of a Fermionic observable as a statistical inference problem. We develop approaches that learn the behaviour of Fermion exchange cycles binned by the conjugacy class of the permutation group (which we term `permutation family'). This extends the work of DuBois, Brown and Alder\cite{dubois2017overcoming} to inhomogeneous and more complex systems. Monte Carlo samples are used to train models for both the probability of a permutation family and the energy of this set of exchange permutations. The overall Fermionic energy is then directly inferred from these models, without using a direct ratio estimator on the Monte Carlo samples. By imposing physical understanding as inductive priors, we produce accurate and useful fits that remain robust even in regimes with severe sign problems. We generalise the linear (ideal-gas style) models of DuBois et al. with Bayesian priors that enforce the intuitive models of Feynman\cite{Feynman1953A} at their asymptotic limits. These linear models serve as the baseline for a Long Short-Term Memory (LSTM) neural network, which is tasked with learning only the residual many-body correlations on top of the physical model. We develop active important sampling methods driven by these models, which direct the Monte Carlo chains toward undersampled permutation regions, to efficiently reduce the variance in the observable. We apply this framework to small experiments on benchmark systems: the spin-polarised uniform electron gas, and electrons in a 2D harmonic confining potential. In both cases we demonstrate that this inference-based framework can extract stable energies in regimes where direct Monte Carlo sampling fails due to the sign problem.

The Matilda effect describes the systematic under-recognition of women's scientific contributions. We investigated its historical evolution by analyzing over 220 million publications (1900-2025) from OpenAlex, inferring the gender of roughly 60 million authors. To quantify this under-recognition, we calculated the ratio between the overall share of female authors and female corresponding authors. Because corresponding authorship denotes intellectual leadership and primary academic credit, systematic exclusion from this role directly measures the Matilda effect. Our analysis reveals that overall female participation rose from approximately 10% in the 1910s to over 40% recently. For most of the 20th century, women were disproportionately excluded from corresponding authorship, confirming a historical Matilda effect. However, this trend inverted over the past two decades, indicating a global decline. Despite this overall progress, distinct disparities persist: the physical sciences and Asian countries lag behind, whereas the health sciences and Latin America approach gender parity.

The bulk damage of $p$-type silicon sensors caused by gamma irradiation with high total ionizing doses has been investigated. The study was carried out on different types of $n^+$-in-$p$ silicon diodes with different oxygen concentrations and silicon bulk resistivities. The Secondary-Ion Mass Spectrometry technique was used to determine the relative concentration of oxygen in the individual samples. The measured diodes were irradiated by a $^{60}$Co gamma source to total ionizing doses ranging from 0.50 up to 8.28 MGy, and annealed for 80 minutes at 60{\deg}C. The main goal of the study was to characterize the gamma-radiation induced displacement damage by measuring IV and CV characteristics, and the evolution of the full depletion voltage with the total ionizing dose. The Transient Current Technique was used to verify the full depletion voltage and to extract the electric field distribution and the sign of the space charge in the silicon diodes irradiated to the lowest and the highest delivered total ionizing doses. The results show a linear increase of the bulk leakage current with the total ionizing dose, with the damage coefficient being dependent on initial resistivity and oxygen concentration of the silicon diode. The effective doping concentration and full depletion voltage decrease significantly with an increasing total ionizing dose, before starting to increase again at a specific dose. We assume that the initial decrease in the effective doping concentration is caused by the effect of acceptor removal. An additional notable finding of this study is that the bulk leakage current and CV characteristics of the gamma-irradiated diodes do not show any evidence of an annealing effect.

We introduce radiative corrections in neutral-current (anti)neutrino-nucleon elastic scattering at $\text{GeV}$ energies within the effective field theory framework. We factorize cross sections into soft and hard functions, clarify the (anti)neutrino flavor dependence at both amplitude and cross-section levels, and improve the quantum chromodynamics (QCD) contributions to low-energy neutral-current processes. The radiative corrections at the single-nucleon level reach a magnitude comparable to the contributions from strange quarks. We also compare our results with the experimental data from BNL E734 and MiniBooNE collaborations, finding excellent agreements with the experimental data.

Characterizing the mechanical response of materials traditionally requires solving optimization problems in which model parameters are calibrated or trained to minimize the discrepancy between model predictions and experimental data. This process can be computationally expensive and time-consuming. To overcome this limitation, we propose two neural operator architectures that directly map experimentally measured data to the constitutive functions governing the mechanical response of the material: Physics-Augmented Neural Operators (PANO) and Constitutive Artificial Neural Operators (CANO). The proposed neural operators approximate the mapping between the infinite-dimensional input space of full-field displacement measurements and net reaction forces, and the infinite-dimensional output space of hyperelastic strain-energy density functions. The displacement fields are encoded through Laplacian eigenfunctions to obtain discretization-independent and noise-robust predictions. Our framework constrains the output space to physically admissible material models that satisfy fundamental physical requirements by design. The neural operators are trained on simulated data tuples of displacement fields and reaction forces for a range of material models. Once trained, the neural operators enable near-instantaneous material characterization and require only a single forward pass to infer the strain-energy density function from a given experimental dataset. We test the predictive power of the neural operators for unseen data, noisy data, data with missing information, data from different spatial discretizations, and data from geometries of different sizes.

Topological concepts are frequently used to describe structured optical fields, including plasmonic near fields. Topological descriptions in terms of skyrmion numbers implicitly assume the compactness of the underlying manifold. Even when skyrmion-like textures appear locally, the compactness is usually not fulfilled in extended optical fields. Here, we use photoemission electron microscopy to investigate a plasmonic nano-focus that exhibits a sequence of radially extending alternating skyrmion and antiskyrmion textures. The full spatio-temporal reconstruction of the electric field vectors and their topology is accessible by vector polarimetry. The experiments confirm the expected oscillatory behavior of the skyrmion number and demonstrate that a global skyrmion number cannot be assigned in such non-compact fields.

Low-overhead quantum error-correction schemes are essential for enabling quantum computation on registers containing multiple logical qubits. For planar architectures with limited nearest-neighbor qubit connectivity, the surface code has emerged as the leading paradigm. Recent theoretical and experimental work has shown that a physical-qubit connectivity of degree three is sufficient to implement fault-tolerant quantum error correction. In this work, we study lattice surgery in the context of such trivalent architectures and introduce scalable circuit constructions to implement it. Compared with the four-valent measurement scheme, the trivalent lattice-surgery protocol reduces the required resources by $\mathcal{O}(d)$ qubits out of a total qubit count of $\mathcal{O}(d^2)$ and by $\mathcal{O}(d)$ two-qubit gates out of a total two-qubit gate count of $\mathcal{O}(d^3)$. We benchmark the logical fidelity of both lattice-surgery schemes in terms of experimentally realistic simulations targeting an implementation with a fluxonium qubit based architecture and find a potential improvement of up to $\approx25\%$ for distance-three. These results open a way for scalable planar trivalent qubit architectures to host a surface-code-based logical quantum processor.

Based on the Kramers-Krong relations, we derive and apply a quite general expression that that relates the low frequency quasi-dc Hall conductivity to the asymptotic high frequency optical Hall response (and its manifestations in Kerr and Faraday effects) for time-reversal symmetry breaking (TRSB) states of matter like ferromagnets and time-reversal symmetry breaking superconductors as well as metals in magnetic field. Parametric plots shows this relation is obeyed exactly for the trivial single-mode case of sharp cyclotron resonance, and approximately for theoretical models for ferromagnets and TRSB superconductors. We also apply it to the experimental data from a variety of ferromagnetic systems and show reasonable agreement there as well. We use the relation to predict, from the size of the spontaneous Kerr effect at the pseudogap temperature of the cuprate superconductors that cuprates should exhibit an anomalous Hall effect of approximately 0.1 Ohm$^{-1}\cdot$cm$^{-1}$. This is a small value, but one within experimental reach and we encourage the search for it. Although not explicitly quantum geometric, our treatment has some similarities to efforts to set bounds on physical quantities based on quantum geometric relations and limited physical information.

We propose a machine-learning-empowered approach to the quantum sensing of the plaquette phase, a gauge-invariant quantity arising in three-level $\Delta$ systems. This phase profoundly affects the system dynamics, breaking coherent population trapping and inducing a non-trivial phase dependence of the dynamics. We demonstrate that a multi-layer perceptron (MLP), trained in a supervised-learning framework, can accurately estimate the plaquette phase from STImulated Raman Adiabatic Passage (STIRAP) population transfer efficiencies measured under different driving conditions, which provide experimentally accessible observables. Our results highlight how the combination of coherent control and machine learning (ML) enables effective phase identification in closed-loop quantum systems, opening new perspectives for quantum technologies, specifically quantum sensing applications including synthetic gauge fields.

We propose the development of a low-frequency Very Long Baseline Interferometry (VLBI) network operating in the 100-350 MHz range, incorporating the Square Kilometre Array Low (SKA-LOW). SKA-LOW is expected to achieve exceptionally high sensitivity within this frequency band. By integrating SKA-LOW with other high-sensitivity radio telescopes located across the Asia-Pacific region, the proposed network is anticipated to deliver up to two orders of magnitude improvement in sensitivity compared to the existing VLBA. While several scientific themes utilizing low-frequency VLBI have already been proposed, we specifically advocate for astrometric studies employing existing VLBI stations to demonstrate the feasibility and scientific potential of this frequency regime. Furthermore, the combination of SKA-LOW with additional radio telescopes will enable high-fidelity imaging observations, significantly enhancing the quality and scope of low-frequency VLBI science.