Papers

A topotactic phase transition involves the transformation of one crystalline solid to another, which may include the loss or gain of material, where the orientation of the parent crystal determines the orientation of the daughter. We set out an experimental approach, based on polyepitaxial thin film deposition, where the precise transformation mechanism in important physico-chemical processes can be revealed in brilliant detail. Here, we find a reversible topotactic transition from (001) cubic UO2 to a (130) orthorhombic U3O8 structure; a >35% expansion/contraction. This remarkable result solves a puzzle that has eluded researchers for decades, and presents a method for determining the mechanism of crystallographic transformation in many other compounds.

Super-resolution (SR), a technique for estimating a high-resolution (HR) image from a low-resolution image, has been used in meteorology for downscaling and resolution enhancement of observations. SR Gaussian process regression with a steering kernel (SRGP-SK) generates more accurate HR images than the original SR Gaussian process regression (SRGP), but it has not yet been applied to meteorological data. This study applied SRGP-SK to radar/rain gauge-analyzed precipitation for convective and stratiform cases and evaluated the results using the structural similarity index (SSIM) and radially averaged power spectral density (PSD). SRGP-SK achieved SSIM values comparable to those of bicubic interpolation and higher than those of SRGP while reconstructing finer precipitation structures; it recovered variations down to a wavelength of 6 km, compared with 8 km for bicubic interpolation. This difference may correspond to an approximately threefold increase in the number of convective cells. Among the kernel functions compared, the Matern 5/2 kernel yielded the highest geometric mean PSD ratio. Further investigation of this point could identify the statistical scaling characteristics of the precipitation field. This study evaluates only two precipitation cases; examining more cases is necessary before SRGP-SK can be applied more broadly.

This study presents a multi-fidelity framework for the systematic optimization of genetic algorithm (GA) hyperparameters. The framework integrates three fidelity levels: high-fidelity Fast Fourier Transform (FFT) homogenization for validation, a medium-fidelity 3D convolutional neural network surrogate for rapid property evaluation, and a low-fidelity Gaussian process (GP) surrogate within a Bayesian optimization (BO) framework to guide the hyperparameter search. Various acquisition functions are evaluated, with logNEI achieving the best performance by effectively accounting for the noise inherent in GA evaluations. The proposed framework identifies hyperparameter configurations that enable a 25-generation GA run to achieve elastic modulus values comparable to those obtained in a full 75-generation optimization. Furthermore, introducing a penalized BO objective significantly reduces the number of required lattices with only minor decreases in absolute achieved elastic modulus, revealing a practical trade-off between performance and the number of structures that must be evaluated. High-fidelity FFT validation verifies the effectiveness of the surrogate-driven optimization strategy. The optimized hyperparameters allow for rapid convergence, eliminate the need for lattice mutation, and reduce the overall computational cost by 24% (from 225 to 171 hours) while preserving mechanical performance. These results demonstrate the potential of multi-fidelity optimization as an efficient and practical approach for GA hyperparameter tuning and future experimental lattice design studies.

Modular miniature robots could provide scalable function in constrained environments, but coordinating many imperfect modules remains difficult when computation, communication and reliability are limited. A central robotics challenge is to coordinate many actuator-sensor modules without assigning a privileged leader, prescribing a fixed gait template, or relying on dense communication. Here we introduce a programmable synchronization-graph framework for modular miniature robots in which each actuator-sensor pair is represented as a network node and locomotor coordination is encoded through graph coupling. Fixed intra-subgraph links synchronize heterogeneous actuator groups, whereas a small number of signed inter-subgraph links program phase relationships between groups. In physical robot collectives with up to nine modules, graph coupling drives the emergence of synchronization, signed links tune the phase difference from in-phase to out-of-phase motion, and floor experiments produce gallop-like and trot-like contact patterns in a five-module robot assembly. Replacing dense all-to-all coupling with sparse d-regular topologies preserves synchronization while reducing the coupling burden. The same graph representation also captures fault tolerance: increasing graph degree increases the number of module deactivations tolerated before desynchronization. Finally, an upper-confidence-bound edge-selection algorithm learns inter-subgraph links that drive the system toward target phase states. In a separate deactivation benchmark, the graph-based controller avoids the leader-specific failure mode observed in centralized leader-follower control and reduces worst-case phase error by about threefold. These results establish programmable network topology as a compact control layer for gait phase programming, online adaptation and robustness to unit loss in modular miniature robots.

Bubbly flows exhibit complex multiscale dynamics, with deformable bubbles interacting through the surrounding liquid and giving rise to strongly coupled kinematic and morphological behavior. We present BubbleSH, a bubbly flows dataset consisting of transient, three-dimensional bubble-swarm dynamics obtained from high-fidelity direct numerical simulations of bubbles rising in a periodic domain. The dataset provides time-resolved bubble trajectories, velocities, and shape evolution, with bubble morphology compactly represented using spherical harmonics. Designed to be lightweight yet physically expressive, the dataset enables data-driven modeling of bubbly flow simulators where shape deformation and bubble-bubble interactions play a central role. We characterize the dataset with bubble kinematics, morphology, and interaction patterns, and introduce evaluation metrics for both trajectory and shape prediction. The sensitivity of bubble-swarm dynamics to local perturbations makes BubbleSH particularly well suited to generative models that learn distributions over possible future trajectories. We evaluate a permutationally and translationally equivariant probabilistic emulator on BubbleSH given the proposed metrics. Therefore, we establish a compact, high-fidelity dataset and a benchmark for developing and evaluating data-driven models of deformable, chaotic multiphase systems.

Efficient optimization and control of photonic computing and communication systems increasingly rely on accurate surrogate models/digital twins. While data-driven models may achieve faster inference than traditional physics-based methods, they typically suffer from poor training data efficiency and limited generalizability. To address this trade-off, physics-informed data-driven modeling has emerged as a powerful hybrid paradigm. This paper presents a comparative analysis of these three modeling paradigms across three test use cases: optical amplifiers, directly modulated lasers, and interferometer meshes. By evaluating model complexity, data efficiency, generalizability, and modularity, this work provides a detailed analysis of the respective trade-offs and highlights the advantages of combining physical insight with data-driven learning.

Porous gallium nitride (GaN) is a mesoporous crystalline material, typically in the form of a thin film on an unlike substrate, prepared by electrochemically etching conductive GaN. The use of porous GaN in electronics and optoelectronics is rapidly expanding, but is held back significantly by a lack of structural control and understanding of the principles of pore formation from underlying electrochemical mechanisms, where high-quality characterisation of pore morphology is essential to understanding these principles. Focused ion beam (FIB) tomography is an invaluable tool for such characterisation, but is hindered greatly by the pore back effect, where unwanted contrast appears in an image due to electrons scattering out from the back wall of a pore. No major attempts to formally quantify or assess the extent of the effect for different tomography experiments has been demonstrated. In this work, we demonstrate an advanced methodology for performing FIB tomography on porous GaN thin films, where the experiment is rotated to image pores perpendicular to the surface of the sample, and introduce new voxel intensity-based formalisms for assessing the impact of the pore back effect based on voxel intensities for individual features and the whole dataset. The new approach, which requires more complex preliminary setup, is found to significantly mitigate the pore back effect in porous GaN thin films with a range of pore morphologies without increasing the total experimental duration. The pore back effect can thus be quantified and mitigated in FIB tomography of porous GaN and similar mesoporous thin films.

The detection of transient gravitational wave signals relies on independent search algorithms that analyse detector data and assign significance measures to candidate events. However, varying performance complicates their interpretation. We use supervised machine learning combined with conformal prediction, a framework to quantify uncertainties, to merge multi-pipeline information into well-calibrated confidence scores. We demonstrate that this approach is robust across different classifier architectures and remains stable when trained on different simulated datasets. When applied to events across the GWTC catalogue up to and including the second part of the fourth observing run, the framework identifies several subthreshold candidates with elevated confidence, including the binary neutron star candidate GW200311_103121. We examine the reliability of these up-rankings, finding evidence that high-confidence predictions correspond to signal-like events. This framework enables simplified systematic candidate assessment for gravitational wave catalogues and real-time alerts by providing a single, well-calibrated confidence measure per candidate.

Cosmic voids provide a distinct low-density region for studying the environmental effects of galaxy properties. Using the SDSS DR7 catalog, we identify non-spherical voids via Voronoi tessellation and the watershed algorithm, and classify void galaxies based on their local volume. We compare and find that void galaxies classified by this method are systematically less massive, fainter, bluer, and have higher specific star formation rate (sSFR) than non-void galaxies and all galaxy samples. We then divide void and non-void galaxies into stellar mass bins to focus on the environmental dependence of $g-r$ color and sSFR. By further classifying galaxies into blue/red and star-forming/quiescent populations, we calculate the ratio of blue to red and star-forming to quiescent for void and non-void galaxies separately. Comparing the ratio of the void value to the non-void value for both metrics presents an overall decreasing trend with stellar mass $M_*$ over the $9.4-10.4$ range in $\log[M_*/\mathrm{M}_\odot]$, indicating a stronger environmental effect in lower-mass systems. These results show that our classification of void galaxies in non-spherical voids based on local volume offers a robust approach for quantifying the influence of underdense environments on galaxy evolution.

Claims about the universality of human concepts have been predominantly assessed through linguistic similarity across languages and cultures. However, words are effective as communication devices because they compress rich experiential variation into shared conventions, potentially obscuring hidden individual and cultural differences in how concepts are mentally represented. Here, we analyse 2.6 billion human-made sketches of common concepts from 236 countries and territories to examine conceptual structure through people's visual imagination. Consistent with recent work on image-based cognition, we find that single concepts unfold into multiple distinct visual exemplars, revealing latent information about similarities and differences in conceptual structure across cultures. This variation is strongest for concepts involving haptic interaction, suggesting that visual imagery reflects variation in embodied experience as much as conventional definitions. Comparing embedding models of sketches with word embedding models across languages, we find that their geometries diverge, with visual representations preserving rich semantic and cultural structure that language models compress. Cross-cultural similarities derived from sketches align 32% more closely with established cultural distances than do text-based measures. Together, these results suggest that patterns of human conceptual universality may depend critically on the modality through which concepts are measured, with large-scale sketching providing a direct, high-resolution probe of conceptual diversity across embodied and cultural dimensions of thought.

Binary stars are key tracers of the dynamical evolution of star clusters and provide important constraints on stellar populations and mass functions. The Magellanic Clouds host clusters spanning a wide range of ages and masses, offering an ideal laboratory to investigate these properties in regimes poorly sampled in the Milky Way. We aim to characterize the binary populations, mass functions, blue straggler content, and structural parameters of intermediate-age Magellanic Cloud clusters, and to explore their dependence on global cluster properties. We analyze HST photometry for 16 clusters obtained with ACS/WFC and WFC3/UVIS. Structural parameters are derived from stellar density profiles. Binary fractions are measured using the binary map technique, focusing on systems with mass ratios q > 0.7. We derive MFs accounting for unresolved binaries and identify candidate BS populations from color-magnitude diagrams. The fraction of binaries with q > 0.7 ranges from 5% in NGC 2121 up to 13% in NGC 2173, with a mass-ratio distribution that is consistent with being flat on average. By combining our results with literature data, we confirm a clear anti-correlation between the core binary fraction and cluster mass, while no significant dependence on cluster age is found. The clusters follow the established relation between age and core radius, although with substantial scatter at fixed age. Within the narrow age range explored here, clusters exhibiting steeper MFs are found to have smaller core radii. We find no evidence for a correlation between the fractions of binaries and BS fractions. These findings are consistent with a scenario in which dynamical evolution plays a primary role in the formation of binary populations. The connection between MF slope and structural parameters provides new constraints on cluster evolution and suggests a link between MF slope and structural evolution.

The mass independent energy reconstruction of cosmic rays is crucial for understanding their origin, acceleration, and propagation. Precise measurement of the primary energy can also lead to better mass classification and could enable energy dependent anisotropy maps for individual elements. The GRAPES-3 experiment located in Ooty consisting of 400 scintillator detector array placed 8 m apart covering an area of 25000 m$^2$ with a dedicated muon detector made of 3712 proportional counters, is designed to do these kinds of measurements. Previously electron size calibration curves have been used to find primary energy in the GRAPES-3 data analysis framework however significantly better precision can be established using graph neural network. Thus, in this work we have implemented a modular and dynamic GNN based reconstruction algorithm that automates feature mapping. We demonstrate how the model is learning by studying its latent space and show that scaling the metric in the latent space can lead to further improvements in response resolution. Fine-tuned strategies are presented and a thorough comparison of the reconstructed energy and bias is done for different fine-tuned models along with studying the resolution variation for different mass groups and shower age.

The GRAPES-3 experiment is a ground-based extensive air shower array which consists of approximately $400$ closely packed plastic scintillator detectors and a large area muon telescope. Estimating the number of associated muons created in an air shower is crucial to understand the properties of primary cosmic rays. The GRAPES-3 muon telescope (G3MT) records these secondary muons, however, the punch-through hadrons can introduce background noise. This study aims to develop a machine learning pipeline to distinguish the tracks of secondary muons and hadrons at G3MT. We have used CORSIKA-simulated proton showers having energy in the range 100-158 TeV as an input for a Geant4-based detector simulation to analyze the signatures of both type of particles. Initially, single-particle classification was performed using decision trees, random forests, neural networks, and XGBoost, with XGBoost achieving the highest accuracy of 88.7%. For multiparticle classification, we modelled Graph Neural Networks (GNNs) where each event was represented as a graph with detector hits as nodes. A GNN with edge convolution layers was developed to classify each node as a muon or hadron hit. Following this, a deep learning regression model using Dynamic Reduction Network was developed to estimate the number of particles and muons striking G3MT simultaneously. Details of the analysis and results of the multiparticle classification task will be presented.

GalPort is a Python package for analysing the orbital dynamics of evolving disc galaxy numerical models in action-angle space. The package implements novel numerical methods for efficiently estimating actions, angles, and frequencies across different, particle-specific timescales: on the scale of radial or vertical oscillation and on the resonant libration/circulation timescale. The algorithm allows calculation of these dynamic quantities simultaneously at all time steps of the simulated galactic evolution. With this tool, one can trace orbital behaviour within time-varying galactic potentials and classify orbits (resonantly trapped, circulating, or passing through a resonance) based on their angle evolution. GalPort also includes specialised options for analysing the phase-space structure of a galactic bar in the disc plane and along its major axis. We demonstrate the package's performance on a typical N-body model of a barred galaxy, obtaining the global distributions of actions and frequencies and performing a detailed orbital decomposition. The code is publicly available under the MIT license at: https://github.com/vdzozulia/galport

We show that Airy function partition functions for M2-brane theories may be derived from relative equivariant localization of quantum M-theory. The eleven-dimensional Chern–Simons coupling gives the cubic term in the grand potential, while the $X_8$ correction gives the charge shift. Fixing the M2-brane charge turns the localized M-theory path integral into an Airy integral. In this way we derive the ABJM result, its toric Calabi–Yau generalizations, and gravitational blocks for black holes and other spacetimes, up to the prefactor and non-perturbative corrections.

Understanding the QCD phase structure and the possible existence of a critical point remains one of the central goals of the heavy-ion program at RHIC. In this proceeding, we present recent STAR results across multiple observables that probe different aspects of the hot and dense matter created in Au+Au collisions. These include two-particle transverse momentum correlations of mean transverse momentum, net-proton cumulants up to fourth order, identical-pion femtoscopy, and baryon-strangeness correlations. We also discuss femtoscopic measurements of baryon-baryon pairs, which provide insight into hyperon-nucleon and hyperon-hyperon interactions and the possible formation of strange dibaryon states. Together, these results provide complementary probes of the system's evolution across a wide collision-energy range (sqrt(sNN) = 3-200 GeV), offering new constraints on the QCD equation of state and the location of the QCD critical point.

The deconfined quantum critical point (DQCP) in square lattice S=1/2 quantum antiferromagnets has been extensively studied with a large body of evidence pointing to a weakly first-order transition scenario. Recent studies, which focused on entanglement at this nearly continuous DQCP in square lattice J-Q models, have observed conflicting bipartite entanglement entropy (EE) scaling behavior. One bipartition choice gave scaling coefficients in remarkable agreement with predictions from the unitary CFT corresponding to the putative DQCP. While another equally natural choice gave scaling coefficients in complete violation of unitary CFT that may be attributed to lack of scale invariance at the known weakly first-order behavior of the model. This motivates the exploration of DQCP behavior via entanglement measures in lattice models with distinct crystalline symmetries. Here we study a S=1/2 honeycomb model that hosts a nearly continuous transition between Néel and valence-bond-solid ground states relevant to probing DQCP. Using large-scale quantum Monte Carlo simulations, we compute the Rényi EE for a variety of bipartitions and test the CFT based description of the DQCP on the honeycomb lattice. For smooth bipartitions, we find no evidence of logarithmic corrections, in accordance with CFT, thereby essentially ruling out contributions from Goldstone modes. For subsystems with corners, CFT predicts universal logarithmic contributions, which we extract for corners with 60 and 120 degree angles and find close agreement with an emergent SO(5) CFT. While we observe scaling consistent with a critical system in the majority of cases, we also demonstrate an intriguing counterexample of the hexagon subsystem that exhibits a subtle period three oscillation. This results in three separate finite-size series, where the sign of the logarithmic term apparently changes depending on the series.

Boundary layer theory and its analytical methods for skin friction coefficients provide an important basis for aerodynamic analysis. However, classical analytical formulas are mostly limited to flat-plate flows. High-fidelity numerical simulations are not only computationally expensive but also yield predictions that are highly sensitive to physical models, numerical schemes, and grid resolution. To overcome these limitations, symbolic AI opens a new pathway to discover novel laws of complex physical systems from data. Using limited data from surface solutions of the Euler equations and the skin friction coefficient from viscous flows over airfoils, we employ symbolic regression to progressively discover a generalizable, interpretable analytical formula chain for fast skin friction prediction in subsonic and supersonic attached flows. From the perspective of physical mechanisms, the discovered analytical expression chain reveals scaling laws for skin friction at different Mach numbers: the basic form captures the logarithmic decay of skin friction along the streamwise direction in the turbulent boundary layer; the inclusion of a pressure coefficient correction term quantifies the effect of surface pressure variation; and the Mach number correction term evolves with flow regimes, transitioning from the compressibility correction term in subsonic regimes to the thermodynamic effects term in supersonic and hypersonic regimes. This knowledge chain exhibits a unified structure across different Mach numbers, and omitting the correction terms under certain conditions recovers classical theoretical forms, further demonstrating its physical consistency. Validation against typical geometries shows that this analytical formula chain achieves a low average integrated skin friction drag prediction error, with good generalization capability across different freestream conditions and geometric shapes.

We discuss noncommutative differential geometry from a vector field centric point of view. This is based on the notion of first order vector field calculus (FOVC), which has been previously introduced by Borowiec under the name Cartan pair. We define the universal FOVC and construct an adjunction between the categories of FOVC and that of first order differential calculi, showing that the vector field approach is dual, though not equivalent, to the differential form one. This correspondence is then extended to covariant vector field and differential calculi. On Hopf algebras, (bi)covariant FOVC are in bijection with (bicovariant) quantum tangent spaces. For Hopf–Galois extensions, quantum tangent spaces give rise to vertical vector fields and in this setup we further describe base vector fields and horizontal vector fields and show that they are related via a noncommutative Atiyah sequence. Multiple examples, based on braided derivations, finite groups and the quantum Hopf fibration, are given. The vector field approach is further enriched by a sheaf-theoretic treatment, which recovers the former as a local, or affine, picture.

This work investigates the thermodynamic behavior of deformed AdS-Schwarzschild black holes by incorporating higher-order corrections within non-interacting spacetime models and extended entropy frameworks. To address the inadequacies of classical statistical mechanics in describing gravitational systems with non-local and long-range interactions, we employ non-extensive entropy formalisms, specifically Tsallis and Barrow entropies, which capture quantum-scale deviations and extended correlations. The resulting thermodynamic analysis reveals significant departures from conventional black hole behavior under strong entropy deformations. Notably, as the degree of non-extensivity decreases, the system asymptotically recovers classical features, indicating an emergent universality across statistical regimes. Furthermore, the Joule-Thomson (JT) expansion is examined to analyze the temperature-pressure response during adiabatic processes. Key thermodynamic quantities, including mass, temperature, heat capacity, Gibbs free energy, enthalpy, internal energy, and the JT coefficient, are computed under the influence of non-extensive entropy corrections. These results provide deeper insight into black hole thermodynamics in quantum-corrected spacetimes and offer new avenues for exploring gravitational systems beyond the traditional Bekenstein-Hawking (BH) framework.

We predict a class of bound states in the continuum (BICs) in non-subwavelength periodic metasurfaces – Rayleigh BICs – that emerge precisely at Rayleigh anomalies, where diffraction channels open. In contrast to conventional symmetry-protected and accidental BICs, which are typically engineered in the subwavelength regime with only a few radiation channels, Rayleigh BICs remain nonradiating far beyond this limit, even when multiple diffraction channels are open. Their formation originates from the interplay between collective lattice resonances, Rayleigh anomalies, and anapole states of the constituent metaatoms. We show that Rayleigh BICs are characterized by an unusual cubic scaling of the quality factor in reciprocal space, ($Q \propto 1/k^3$), an asymptotic behavior that is not characteristic of either symmetry-protected or accidental BICs. Our results establish a constructive route for engineering nonradiating states in metasurfaces beyond the subwavelength regime.

The quantum boomerang effect (QBE) is a unique dynamical signature of Anderson localization, characterized by a launched wavepacket that initially drifts but ultimately returns to its initial position due to fundamental quantum interference. In this work, we theoretically establish and quantitatively characterize the QBE in a time-crystalline structure using a periodically driven quantum particle in a one-dimensional potential well. By constructing maximally localized Floquet-Wannier states and introducing temporal disorder, we rigorously map the continuous Floquet dynamics onto a discrete disordered tight-binding lattice. By positioning a detector at a fixed spatial coordinate, we monitor the temporal evolution of the wavepacket, to extract the mean temporal center of mass of the probability density in a time-crystalline structure. This mean temporal center of mass exhibits an initial ballistic expansion, followed by a pronounced U-turn, and ultimately returns to its initial temporal position after long-time evolution. These results confirm the existence of the complete QBE in the time domain. They also demonstrate that non-trivial dynamics can be explored within time-crystalline systems, even though these structures already possess an inherent temporal periodicity.

Electromagnetic waves are intrinsically vectorial and require description via polarization, unlike scalar fields such as acoustic pressure or electronic wavefunctions. In three dimensions, the transversality constraint further prevents any globally smooth transverse-polarization frame at the $\Gamma$ point, which would apparently rule out a simple scalar band structure for three-dimensional (3D) photonic crystals. We show here that site-adapted $p$-orbitals can realize scalar-wave dispersion: the induced band representation is isomorphic to the scalar elementary band representation up to a one-dimensional character twist, so the symmetry-enforced degeneracies and compatibility relations are the same. We demonstrate this mechanism experimentally in 3D photonic meta-crystals, where the local $p$-orbital axes adapt from site to site according to symmetry. In contrast to a fixed-polarization reduction (e.g., in 2D), our construction preserves site-polarization textures while simultaneously supporting a scalar network with one amplitude per site. Thus, it offers a pathway from vectorial photonic degrees of freedom to scalar band engineering, keeping polarization as an active design knob.

We study a quantum implementation of non-Abelian Thouless pumping in Lieb lattices using Rydberg synthetic dimensions. The lattice is encoded in twelve selected microwave-coupled Rydberg levels, forming a three-cell structure with six degenerate zero-energy states. These zero-energy states define the working subspace for cyclic modulation of the microwave couplings, while the remaining bright states provide the dominant leakage channels at finite evolution time. To choose the relative timing of the Gaussian pulses, we introduce a global adiabatic criterion (GAC), which evaluates the mean value and temporal fluctuation of a nonadiabatic factor obtained from a representative $\Lambda$-type transfer paradigm. With the resulting timing applied to the full twelve-level pumping dynamics, composing two elementary pumping cycles in opposite temporal orders produces distinct projected population maps. It is exactly consistent with noncommuting matrix-valued adiabatic operations in the zero-energy subspace. We numerically simulate the non-Abelian Thouless pumping using the Lindblad master equation with state-dependent Rydberg loss and representative perturbations. The results show that the GAC-selected timing within the same Gaussian pulse family gives higher target-state population than two literature-adapted Gaussian pulse schedules over the simulated parameter ranges. This quantum implementation of non-Abelian Thouless pumping, enabled by the GAC, marks a major milestone in finite-time geometric control and paves the way for transformative applications in holonomic quantum computing with Rydberg synthetic lattices.

This short report explores the (non exhaustive) current state of quantum technologies, their potential applications, and the challenges that must be addressed to harness their full potential. In particular we will focus on the quantum computer architecture and its ecosystem. Such architecture represents a complex, multi-layered system that integrates quantum and classical components to enable the execution of quantum algorithms. This manuscript is then organized as follows : first we will introduce the quantum processing unit, the lowest layer of a quantum computer. Then we will progress from the lowest to the higher layer of the system architectures : measurement, circuit control, error correction and mitigation system, the quantum compiler and finally the software stack, with particular emphasis on the interactions between these components

Molecular passivation has become central to reducing photovoltage losses in metal-halide perovskite solar cells, but its electronic action is still often inferred from device-level metrics rather than directly resolved at the nanoscale. Here, we use amplitude-modulated Kelvin probe force microscopy to examine how [3-(2-aminoethylamino)propyl]trimethoxysilane (AEAPTMS) modifies the surface potential and photovoltage dynamics of mixed-cation, mixed-halide perovskite thin films. AEAPTMS homogenises the dark contact potential difference (CPD), narrowing its distribution from ~45.7 to ~14.6 mV without obvious morphological changes. Under illumination, passivated films show a larger steady-state surface photovoltage (SPV) and faster stabilisation, with the SPV increasing from ~345 to ~417 mV and the stabilisation time constant decreasing from ~840 to ~470 s. Wavelength-dependent SPV further indicates reduced sub-bandgap electronic disorder. By separating grain-boundary and grain-interior contributions, we show that AEAPTMS suppresses grain-boundary potential barriers, linking amino-silane passivation to a more homogeneous and stable carrier landscape.

We demonstrate microsecond-timescale electrical control of the terahertz (THz) emission phase in broadband field-free spintronic THz emitters, enabling megahertz-rate phase inversion while overcoming the kilohertz limitations of conventional mechanical and field-driven approaches. Our device integrates an H-dipole antenna with a spintronic heterostructure exhibiting uniaxial magnetic anisotropy, enabling deterministic spin-orbit torque induced in-plane magnetization switching without external magnetic fields. The corresponding THz phase inversion is directly observed in the time domain signal, by applying $1\,\mu \mathrm{s}$ electrical pulses on the bias striplines of the H-dipole. This field-free operation reduces system complexity while significantly extending modulation bandwidth. Our results establish electrically programmable spintronic THz emitters that could be used to develop a compact and scalable platform for integrated on-chip THz devices and ultrafast applications, including phase-sensitive spectroscopy and near-field imaging, where high-speed and precise control of THz waveforms is essential.

A liquid drop falling into a pool can create a vortex ring at the right impact conditions. Such drop-formed vortex rings are of importance in nature and technology and the dynamics of rings created by single and synchronised double drop impacts have been extensively studied. In practice, two neighbouring drops rarely impact a liquid surface exactly at the same time, yet the interaction of two unsynchronised vortex rings have not been studied. Here, we have performed experiments with two water drops impacting a water pool at varying time differences $\Delta t$. By using particle image velocimetry, we have quantified the time-evolution of the resulting vortex rings. We find four distinct categories of vortex ring evolution depending on $\Delta t$. At $\Delta t<0.5$ ms, fully symmetric merging of the vortex rings occur. At unsynchronisation larger than this, the merging either becomes asymmetric and incomplete ($0.5 < \Delta t<7$ ms) or non-existent ($\Delta t \geq 7$ ms). At $7 \leq \Delta t<80$ ms, the creation of the second vortex ring is impaired, whereas at $\Delta t\geq 80$ ms, the first impact no longer affects the formation of the second ring and the two rings evolve without influencing each other. We show that these regimes can be explained by the capillary waves created by the first droplet. Our results demonstrate the importance of the time difference between drop impacts in the creation and subsequent interaction of two adjacent drop-formed vortex rings, which is important for achieving uniform and controlled mixing in high-throughput applications.

For three-flavor neutrino oscillations in constant-density matter, the Cayley–Hamilton theorem forces the evolution operator into a quadratic polynomial in $\hat{H}$, with coefficients determined by the three real eigenvalues through a Vandermonde system we write out explicitly. The eigenvalues follow from Cardano's trigonometric formula, recovering the Zaglauer–Schwarzer expressions. The Denton–Minakata–Parke (DMP) approximation achieves fractional accuracy better than $10^{-4}$ because its $1$–$3$ rotation is a resummation: it removes the near-degeneracy that makes the naive expansion diverge at $\hat{A}\to 1$, replacing the unbounded $(1-\hat{A})^{-1}$ with an effective parameter $\epsilon_0\lesssim 0.015$ bounded uniformly in energy. A density-matrix treatment with a Lindblad term handles open-system decoherence and wave-packet effects in the same language; matter-dressed coherence lengths satisfy $L/L^{ij}_{\rm coh}\sim 10^{-3}$–$10^{-2}$ for terrestrial baselines. The CP asymmetry $\mathcal{A}_{\rm CP}(\nu_\mu\to\nu_e)$ is split into genuine and matter-induced fake contributions. Closed-form Jacobians in the NuFIT~6.0 parameter basis feed Monte Carlo and linearized uncertainty-propagation schemes, the latter validated against a Feldman–Cousins profile-likelihood mapping near physical boundaries. The Denton–Parke NuFast-LBL algorithm [Phys.\ Rev.\ D 110, 073005 (2024)] remains the tool of choice for production fits; the analytic expressions here supply what iterative solvers cannot – parameter continuity, transparent limits, and Jacobians in closed form.

A metallic point contact formed on a non-superconducting or weakly superconducting material often nucleates or enhances superconductivity confined under the contact. However, no unified theoretical description of the phenomenon exists. We show that the spatially inhomogeneous, predominantly uniaxial nature of the stress field under a point contact is fundamental for such tip-induced and tip-enhanced superconductivity (TISC/TESC). We also show that the coupling of such a stress field to the electronic structure can be estimated through an experimentally measurable uniaxial coupling scale $C^{\mathrm{exp}}$. Combining Hertzian contact mechanics with a Ginzburg-Landau variational analysis, we derive a criterion for the nucleation of TISC/TESC and determine $C^{\mathrm{exp}}$ for twenty-one materials. For topological semimetals with ungapped band crossings, the framework explains observed critical temperatures with no free parameters and for all others, $C^{\mathrm{exp}}$ provides a direct experimental determination of the uniaxial strain sensitivity and a target scale for microscopic theories.The work predicts TISC in elemental Sb and Y with $T_c \approx 2.8$\,K and $T_c \approx 12$\,K respectively.