Papers

The remarkable progress of artificial intelligence (AI) has revealed the enormous energy demands of modern digital architectures, raising deep concerns about sustainability. In stark contrast, the human brain operates efficiently on only ~20 watts, and individual cells process gigabit-scale genetic information using energy on the order of trillionths of a watt. Under the same energy budget, a general-purpose digital processor can perform only a few simple operations per second. This striking disparity suggests that biological systems follow algorithms fundamentally distinct from conventional computation. The framework of information thermodynamics-especially Maxwell's demon and the Szilard engine-offers a theoretical clue, setting the lower bound of energy required for information processing. However, digital processors exceed this limit by about six orders of magnitude. Recent single-molecule studies have revealed that biological molecular motors convert Brownian motion into mechanical work, realizing a "demon-like" operational principle. These findings suggest that living systems have already implemented an ultra-efficient information-energy conversion mechanism that transcends digital computation. Here, we experimentally establish a quantitative correspondence between positional information (bits) and mechanical work, demonstrating that molecular machines selectively exploit rare but functional fluctuations arising from Brownian motion to achieve ATP-level energy efficiency. This integration of information, energy, and timescale indicates that life realizes a Maxwell's demon-like mechanism for energy-efficient information processing.

Geometry can fundamentally govern the propagation of light, independent of material constraints. Here, we demonstrate that a fractal phase space, endowed with a non-Euclidean, scale-dependent geometry, can intrinsically induce resonance quantization, spatial confinement, and tunable damping without the need for material boundaries or external potentials. Employing a fractional formalism with a fixed scaling exponent, we reveal how closed-loop geodesics enforce constructive interference, leading to discrete resonance modes that arise purely from geometric considerations. This mechanism enables light to localize and dissipate in a controllable fashion within free space, with geometry acting as an effective quantizing and confining agent. Numerical simulations confirm these predictions, establishing geometry itself as a powerful architect of wave dynamics. Our findings open a conceptually new and experimentally accessible paradigm for material-free control in photonic systems, highlighting the profound role of geometry in shaping fundamental aspects of light propagation.

Using particle-resolved molecular-dynamics simulations, we compute the phase diagram for soft repulsive spherocylinders confined on the surface of a sphere. While crystal (K), smectic (Sm), and isotropic (I) phases exhibit a stability region for any aspect ratio of the spherocylinders, a nematic phase emerges only beyond a critical aspect ratio lying between 6.0 and 7.0. As required by the topology of the confining sphere, the ordered phases exhibit a total orientational defect charge of +2. In detail, the crystal and smectic phases exhibit two +1 defects at the poles, whereas the nematic phase features four +1/2 defects which are connected along a great circle. For aspect ratios above the critical value, lowering the packing fraction drives a sequence of transitions: the crystal melts into a smectic phase, which then transforms into a nematic through the splitting of the +1 defects into pairs of +1/2 defects that progressively move apart, thereby increasing their angular separation. Eventually, at very low densities, orientational fluctuations stabilize an isotropic phase. Our simulations data can be experimentally verified in Pickering emulsions and are relevant to understand the morphogenesis in epithelial tissues.

Light is the fundamental medium through which we perceive the world around us. In the modern era, light can not only be used in its raw form but can also be used as a versatile tool. Generally, light fields carry energy and momentum (both linear and angular). Due to the transfer of linear momentum from light to matter, the radiation pressure is exerted, whereas, the intrinsic spin angular momentum (SAM) is associated with the polarization states of light. Light fields embedded with optical orbital angular momentum (OAM) – also known as optical vortices or phase singular beams – have truly revolutionized the field of optics and extended our basic understanding of the light-matter interaction process across various scales. Optical vortices – spatially characterized by the presence of twisted phase fronts and a central intensity null – have found a myriad of applications starting from microparticle trapping and manipulation to microscopy, optical communication, and quantum information science, among others. Here, we revisit some of the fundamental concepts on optical vortices and discuss extensively on how this new dimension of light i.e., the OAM, has been exploited in both linear and nonlinear optical regimes. We discuss the different types of vortex beams, the techniques used to generate and detect their OAM, and their propagation. Particularly, we put a special emphasis on the utilization of vortex beams in nonlinear regimes to explain different optical phenomena such as the second harmonic generation, parametric down-conversion, and high-order harmonic generation. The generation of vortex beams in the UV to XUV regimes, encoded with higher OAM values, could potentially extend their application range to areas such as high-capacity data transmission, stimulated emission depletion microscopy, phase-contrast imaging, and particle trapping in optical tweezers, among others.

We investigate the evolution of red supergiant (RSG) progenitors of core-collapse (CC) supernovae (SNe) with initial masses between $12-20~M_\odot$ focusing on the effects of enhanced mass loss due to pulsation-driven instabilities in their envelopes and subsequent dynamical ejections during advanced stages of nuclear burning. Using time-dependent mass loss from detailed MESA stellar evolution models, including a parameterized prescription for pulsation-driven superwinds and time-averaged mass loss rates attributed to resulting shock-induced ejections, we construct the circumstellar medium (CSM) before the SN explosion. We calculate resulting CSM density profiles and column densities considering the acceleration of the stellar wind. Our models produce episodes of enhanced mass loss $10^{-4}-10^{-2}~M_\odot~\rm{yr}^{-1}$ in the last centuries-decades before explosion forming dense CSM ($>10^{-15}~\rm{gcm}^{-3}$ at distances $<10^{15}$ cm) – consistent with those inferred from multi-wavelength observations of Type II SNe such as SN~2023ixf and SN~2020ywx.

Flare ribbons with parallel and circular morphologies are typically associated with different magnetic reconnection models, and the simultaneous observation of both types in a single event remains rare. Using multi-wavelength observations from a tandem of instruments, we present an M8.2-class flare that occurred on 2023 September 20, which produced quasi-parallel and semi-circular ribbons. The complex evolution of the flare includes two distinct brightening episodes in the quasi-parallel ribbons, corresponding to the two major peaks in the hard X-ray (HXR) light curve. In contrast, the brightening of semi-circular ribbons temporally coincides with the local minimum between the two peaks. Using potential field extrapolation, we reconstruct an incomplete dome-like magnetic structure with a negative polarity embedded within the northwestern part of the semi-circular positive polarity. Consequently, the magnetic configuration comprises two sets of field lines with distinct magnetic connectivities. We suggest that the standard flare reconnection accounts for the two-stage brightening of quasi-parallel ribbons associated with the two HXR peaks. Between the two stages, this process is constrained by the interaction of eruptive structures with the dome. The interaction drives the quasi-separatrix layer reconnection, leading to the brightening of semi-circular ribbons. It also suppresses the standard flare reconnection, resulting in a delayed second HXR peak.

We investigate temporal scattering in lossless Drude media and reveal an overlooked role of the zero-frequency flat band associated with static polarization charge. This flat band forms an exceptional line spanning all wavenumbers and can be directly excited during temporal scattering at photonic time interfaces, generating non-propagating static fields alongside the usual reflected and transmitted waves. Eigenvector coalescence at the corresponding exceptional points leads to two distinctive features absent in previously studied systems: a static mode whose amplitude increases linearly with time, and an additional static component arising from the system's generalized eigenvector. Remarkably, these effects occur without violating total energy conservation, underscoring the Hermitian nature of the dynamics. Our findings present a new physical picture of temporal scattering, sharply distinct from that in dispersionless and Lorentz-dispersive media.

This paper reports an innovative concept of “plasma fibre" using bright-core helicon plasma, inspired by its spatial and spectral similarities to the well-known optical fibre. Theoretical analyses are presented for both ideal case of step-like density profile and the realistic case of Gaussian density profile in radius. The total reflection of electromagnetic waves near the sharp plasma density gradient and consequently the wave-guide feature could indeed happen if the incident angle is larger than a threshold value. Numerical computations using electromagnetic solver that based on Maxwell's equations and cold-plasma dielectric tensor yield consistent results. The experimental verification and prospective applications are also suggested. The “plasma fibre" could be functional component that embedded into existing communication systems for special purpose based on its capability of dynamic reconfiguration.

Deep learning-based surrogate models offer a computationally efficient alternative to high-fidelity computational fluid dynamics (CFD) simulations for predicting urban wind flow. However, conventional approaches usually only yield low-frequency predictions (essentially averaging values from proximate pixels), missing critical high-frequency details such as sharp gradients and peak wind speeds. This study proposes a hierarchical approach for accurately predicting pedestrian-level urban winds, which adopts a two-stage predictor-refiner framework. In the first stage, a U-Net architecture generates a baseline prediction from urban geometry. In the second stage, a conditional Generative Adversarial Network (cGAN) refines this baseline by restoring the missing high-frequency content. The cGAN's generator incorporates a multi-scale architecture with stepwise kernel sizes, enabling simultaneous learning of global flow structures and fine-grained local features. Trained and validated on the UrbanTALES dataset with comprehensive urban configurations, the proposed hierarchical framework significantly outperforms the baseline predictor. With a marked qualitative improvement in resolving high-speed wind jets and complex turbulent wakes as well as wind statistics, the results yield quantitative enhancement in prediction accuracy (e.g., RMSE reduced by 76% for the training set and 60% for the validation set). This work presents an effective and robust methodology for enhancing the prediction fidelity of surrogate models in urban planning, pedestrian comfort assessment, and wind safety analysis. The proposed model will be integrated into an interactive web platform as Feilian Version 2.

The duration of isolated attosecond pulses created via high-order harmonic generation is determined by the number of optical cycles in the driving laser. Achieving shorter attosecond soft X-ray pulses requires minimizing the number of cycles while maintaining a high pulse energy. Here, we demonstrate a carrier-envelope-phase-stable, 100-mJ-class sub-cycle mid-infrared laser that produces a supercontinuum coherent soft X-ray with unprecedented bandwidth. The system delivers 50-mJ, 6.7-fs (0.88-cycle) pulses at a center wavelength of 2.26 $\mu$m - over two orders of magnitude more energetic than any previous sub-cycle laser. We applied the system to high-order harmonic generation and compared the results to simulations based on the three-dimensional time-dependent Schrödinger equation to identify unique features of sub-cycle lasers. This work represents a decisive step toward high-energy half-cycle lasers and high-energy single-digit attosecond soft X-ray pulses that can be used to probe matter and light-matter interactions at previously inaccessible temporal resolutions.

Neuromorphic computing demands synaptic elements that can store and update weights with high precision while being read non-destructively. Conventional ferroelectric synapses store weights in remnant polarization states and might require destructive electrical readout, limiting endurance and reliability. We demonstrate a ferroelectric MEMS (FeMEMS) based synapse in which analog weights are stored in the piezoelectric coefficient $d_{31,eff}$ of a released Hf$_{0.5}$Zr$_{0.5}$O$_2$ (HZO) MEMS unimorph. Partial switching of ferroelectric domains modulates $d_{31,eff}$, and a low-amplitude mechanical drive reads out the weight without read-disturb in the device yielding more than 7-bit of programming levels. The mechanical switching distribution function follows a Lorentzian distribution as a logarithmic function of partial poling voltage ($V_p$) consistent with nucleation-limited switching (NLS), and the median threshold extracted from electromechanical data obeys a Merz-type field-time law with a dimensionless exponent $\alpha = 3.62$. These relationships establish a quantitative link between mechanical weights and electrical switching kinetics. This mechanically read synapse avoids depolarization and charge-injection effects, provides bipolar weights (well suited for excitatory and inhibitory synapses), directly reveals partial domain populations, and offers a robust, energy-efficient route toward high-bit neuromorphic hardware.

Nanophase metallic iron ( $\mathrm{npFe}^0$ ) is a key indicator of space weathering on the lunar surface, primarily attributed to solar wind irradiation and micrometeoroid impacts. Recent discoveries of hematite ( $\mathrm{Fe}_2 \mathrm{O}_3$ ), a highly oxidized form of iron, in the lunar polar regions challenge the prevailing understanding of the Moon's reducing environment. This study, using ReaxFF molecular dynamics simulations of micrometeoroid impacts on fayalite ( $\mathrm{Fe}_2 \mathrm{SiO}_4$ ), investigates the atomistic mechanisms leading to both reduced and oxidized iron species. Our simulations reveals that the high-temperature and pressure conditions at the impact crater surface produces a reduced iron environment while providing a transient oxygen-rich environment in the expanding plume. Our findings bridge previously disparate observations-linking impact-driven $\mathrm{npFe}^0$ formation to the puzzling presence of oxidized iron phases on the Moon, completing the observed strong dichotomous distribution of hematite between the nearside and farside of the Moon. These findings highlight that micrometeoroid impacts, by simultaneously generating spatially distinct redox environments, provide a formation mechanism that reconciles the ubiquitous identification of nanophase metallic iron ( $\mathrm{npFe}^0$ ) in returned lunar samples with $\mathrm{Fe}^{3+}$ signatures detected by remote sensing. This underscores the dynamic nature of space weathering processes. For a more nuanced understanding of regolith evolution, we should also consider the presence of different generations or types of $\mathrm{npFe}{ }^0$, such as those formed from solar wind reduction versus impact disproportionation.

Quantum communication is a growing area of research, with quantum internet being one of the most promising applications. Studying the statistical properties of this network is essential to understanding its connectivity and the efficiency of the entanglement distribution. However, the models proposed in the literature often assume homogeneous distributions in the connections of the optical fiber infrastructure, without considering the heterogeneity of the network. In this work, we propose new models for the quantum internet that incorporate this heterogeneity of node connections in the optical fiber network, analyzing how this characteristic influences fundamental metrics such as the degree distribution, the average clustering coefficient, the average shortest path and assortativity. Our results indicate that, compared to homogeneous models, heterogeneous networks efficiently reproduce key structural properties of real optical fiber networks, including degree distribution, assortativity, and hierarchical behavior. These findings highlight the impact of network structure on quantum communication and can contribute to more realistic modeling of quantum internet infrastructure.

Weather extremes pose major societal risks, especially in a changing climate, but due to their rarity, they are difficult to study using limited observations or complex climate models. We introduce AI+RES, a framework coupling fast AI weather forecasts with a high-fidelity physics model using a rare-event algorithm to efficiently characterize extremes. This approach enables the study of the statistics and physics of very rare events, such as once per millennium heatwaves at two orders-of-magnitude lower computational cost. AI+RES can be applied broadly across climate science and other fields concerned with rare events.

In this work, we benchmark \simulacra's synthetic data generation pipeline against a state-of-the-art Microsoft pipeline on a dataset of small to large systems. By analyzing the energy quality, autocorrelation times, and effective sample size, our findings show that Simulacra's Large Wavefunction Models (LWM) pipeline, paired with state-of-the-art Variational Monte Carlo (VMC) sampling algorithms, reduces data generation costs by 15-50x, while maintaining parity in energy accuracy, and 2-3x compared to traditional CCSD methods on the scale of amino acids. This enables the creation of affordable, large-scale ab-initio datasets, accelerating AI-driven optimization and discovery in the pharmaceutical industry and beyond. Our improvements are based on a novel and proprietary sampling scheme called Replica Exchange with Langevin Adaptive eXploration (RELAX).

In the field of deep space radiation shielding design, traditional high-Z metals are being progressively replaced by novel low-Z materials such as hydrogenated graphene foam, polyethylene-carbon nanotube composite fibers, and boron-rich hydrogen-containing metal-organic frameworks. This transition stems from the constraints of the "gram-scale weight reduction" bottleneck. However, the mechanisms behind these materials' outstanding "lightweight performance" remain at the purely phenomenological level. To address this issue, this paper innovatively proposes a ternary coupled semi-empirical model, with "proton density" (rho_p) as the core independent variable (equivalent to electron density), establishing correlations with full absorption threshold (Eth) and proton utilization rate (eta_p). To validate the model's practicality in complex system design, we embedded it into the NSGA-II genetic algorithm (POP=20, MAXGEN=10). Under constraints of 2-5 layer structures with total thickness <=1 cm, the model's predicted optimal solutions showed an average design error of only 6.2%-8.2% compared to Geant4 heavy simulation dose results. This study provides the first quantitative physical explanation of the inherent trade-off between low-Z materials' "mass savings" and high-Z materials' "space savings." When rho_p<1 mol cm^-3, materials demonstrate extremely high proton utilization rates (eta_p reaching 60-100 MeV cm^-2 mol^-1), achieving 35%-55% mass savings. Conversely, when rho_p>1 mol cm^-3, although eta_p drops below 30 MeV cm^-2 mol^-1, its exceptionally high full absorption threshold (Eth) enables ultra-thin shielding technology. This work offers a robust, physically interpretable, and scalable design tool for multi-objective optimization of area density, thickness, and dose in deep space missions.

Neutrophils play a central role in immunothrombosis through the formation of neutrophil extracellular traps (NETs), a process known as NETosis. Upon stimulation, neutrophils release decondensed chromatin structures enriched with proteolytic enzymes, which contribute to thrombus formation. NETosis is critically dependent on reactive oxygen species (ROS), making redox regulation a key point of intervention. The intrinsic redox cycling of cerium oxide nanoparticles (CNPs) imparts self-regenerating antioxidant properties suitable for modulating neutrophil-driven oxidative stress. To address both the prevention and clearance of NETs, we developed dual-functional CNPs conjugated with DNase I. These engineered nanoparticles were efficiently internalized by neutrophils, reduced intracellular ROS levels, and inhibited NETs formation. In addition, DNase I-functionalized CNPs degraded pre-formed NETs. This dual-action strategy offers a promising nanotherapeutic platform for mitigating NETs-associated thrombotic pathologies. Ongoing studies aim to enhance thrombus targeting and assess in vivo efficacy.

Hydrogen bonds play a pivotal role in chemistry, biology, and condensed-matter physics, where quantum tunnelling can strongly influence structure and dynamics. Isotope substitution (H $\rightarrow$ D) provides a sensitive probe of such tunnelling, but theoretical descriptions often rely on purely numerical models or simplified potentials that obscure physical interpretation. Here we employ a Cornell-type potential combined with a double-well Schrödinger approach to investigate proton and deuteron tunnelling across hydrogen bonds. The model yields semi-analytical wavefunctions and tunnelling splittings that transparently capture isotope-dependent quantum effects. We present scaling behaviour of tunnelling splittings with isotope mass, discuss the influence of barrier width and curvature, and compare model trends with representative experimental and computational results. Beyond hydrogen bonding, the framework provides a general methodology for modelling tunnelling in double-well systems relevant to spectroscopy, enzymatic catalysis, and materials applications.

The magnetic Rayleigh-Taylor instability (MRTI) governs plasma mixing and transport in diverse systems. Owing to computational constraints, two-dimensional (2D) MRTI simulations are common. It is intuitive that 2D and three-dimensional (3D) counterparts differ significantly. One of the crucial differences is the types of wave modes admissible in the two systems. Perturbations in 3D MRTI facilitate the coexistence of interchange, undular, and mixed modes. Such rich physics is not possible in 2D, where the perturbations can only have undular modes. This raises the question, how do the interchange and mixed modes change the MRTI dynamics? To address this aspect, we simulate MRTI in 2D and 3D, across different magnetic field strengths. Consequently, this paper provides, currently lacking, a rigorous qualitative and quantitative evaluation of the similarities and differences between 2D and 3D MRTI. The 2D MRTI is characterized by large-scale plumes with inefficient mixing and significant fluid interpenetration, while the 3D is characterized by significant mixing and small-scale plumes. Consequently, higher gravitational potential energy is extracted in 2D. The small-scale vortices and large current sheets lead to higher energy dissipation in 3D. Extending to fundamental nature of evolution, 3D simulations tend towards a self-similar state (due to interchange and mixed modes). But such behaviour was not observed in our 2D simulations, due to the persistent influence of magnetic field. Surprisingly, the reconnection driven magnetic-to-kinetic energy conversion is similar in 2D and 3D. Overall, the work concludes that interchange and mixed modes influence the dynamics significantly leading to stark differences between 2D and 3D MRTI. The work thus guides the extent to which one can extrapolate the 2D simulations results to understand MRTI in astrophysical and fusion energy systems.

We show that both temporal and spatial symmetry breaking in canonical K-type transition arise as organized hydrodynamic structures rather than stochastic fluctuations. Before the skin-friction maximum, the flow is fully described by a periodic, spanwise symmetric, harmonic response to the Tollmien-Schlichting wave, forming a spatially compact coherent structure that produces hairpin packets. This fundamental harmonic response may visually resemble turbulence, but remains fully periodic and delimits the exact extent of the deterministic regime. A distinct regime change occurs after this point; a hierarchy of new (quasi-)periodic and aperiodic space-time structures emerges, followed shortly by anti-symmetric structures that develop similarly despite no anti-symmetric inputs, marking the onset of aperiodicity and spanwise asymmetry. We identify these structures as symmetry-decomposed spectral and space-time proper orthogonal modes that resolve the full progression from deterministic to broadband dynamics. The key insight is that laminar-turbulent transition can be viewed as a sequence of symmetry breaking events, each driven by energetically dominant, space-time coherent modes that gradually turn an initially harmonic flow into broadband turbulence.

Classical linear optics posits that at sufficiently low intensities, light propagation in dielectric media is governed solely by their linear susceptibilities. Here, we demonstrate a departure from this paradigm in high-Q microresonators, where prolonged photon confinement enables rare quantum electrodynamical (QED) events, mediated by the quantum vacuum, to embed distinctive Raman signatures of the coupled analyte into the resonator's linear transmission spectrum despite their absence from the linear susceptibility. We further show that increasing the amount of adsorbed analyte amplifies these Raman fingerprints well above typical noise floors, rendering them experimentally accessible with state-of-the-art photonic architectures and detection schemes. This novel weak-coupling cavity-QED effect offers unique routes to harness extended photon lifetimes and constrained geometries for leveraging vacuum fluctuations in next-generation photonic technologies for chemical and biological sensing and high-precision optical spectroscopy.

We present a framework for Large Eddy Simulations (LES) with Immersed Boundaries (IBs) to simulate high Reynolds number flows over complex walls. In this approach, which we call Immersed Boundary-Modeled LES (IBMLES), we volume-filter the Navier-Stokes equations to derive the IB bodyforce. This also gives rise to the subfilter-scale (SFS) stress and residual viscous stress tensors, the latter of which is closed, and we expect the SFS stress can be closed with existing models. We show that the IB bodyforce can be closed by modeling the wall-slip velocity and provide two such models. The first is an algebraic model based on volume-filtering the Van-Driest velocity profile, and the second is a slip-length model. We perform an a priori analysis by volume-filtering direct numerical simulation (DNS) data of turbulent channel flow at $\mathrm{Re}_{\tau}=5200$ to inform these models and investigate the behavior of the other terms in this formulation. We find streamwise wall-slip velocity is significant and both models show good agreement with volume-filtered DNS data on average. Slip velocity is non-uniform and retains a signature of inner or large scale flow structures depending on filter size. SFS terms are analogous to those in traditional LES and can likely be modeled with existing SFS models such Dynamic-Smagorinsky. Residual viscous stress is significant, so it must be considered in IBMLES. We perform filtering with multiple filter types and find there is little sensitivity to the choice of filter kernel so long as it abides by the assumptions given in this framework. Based on these results, we propose a dynamic procedure for determining slip-length that will be assessed in future studies.

In this paper, we propose a novel fully programmable linear photonic processor, which we call LightPro, with improved scalability, performance, and footprint. At the heart of LightPro are compact, low-loss, and programmable silicon photonic (SiPh) directional coupler (DC) devices that deploy phase-change material (PCM) for programming the DC's splitting ratio. By thermally inducing phase transitions in the PCM, the coupling coefficient of the DC can be dynamically adjusted to achieve different splitting ratios in the device output. Building on this device foundation, we develop a neural architecture search (NAS) and pruning algorithm to optimize the architecture of the processor for performing MVM operations. Our simulation results show that LightPro achieves up to an 85% reduction in footprint and more than 50% improvement in power consumption. In addition, LightPro is evaluated by performing inference with weight matrices trained on MNIST and linearly separable Gaussian datasets, showing less than a 5% drop in accuracy when scaling up the network. Prototyping results, using a commercial photonic processor (iPronics SmartLight), show LightPro's efficiency and performance (e.g., computational accuracy) compared to conventional photonic MVM hardware, demonstrating the experimental evaluation and feasibility of LightPro for next-generation photonic AI accelerators.

The classical Maximum-Entropy Principle (MEP) based on Shannon entropy is widely used to construct least-biased probability distributions from partial information. However, the Shore-Johnson axioms that single out the Shannon functional hinge on strong system independence, an assumption often violated in real-world, strongly correlated systems. We provide a self-contained guide to when and why practitioners should abandon the Shannon form in favour of the one-parameter Uffink-Jizba-Korbel (UJK) family of generalized entropies. After reviewing the Shore and Johnson axioms from an applied perspective, we recall the most commonly used entropy functionals and locate them within the UJK family. The need for generalized entropies is made clear with two applications, one rooted in economics and the other in ecology. A simple mathematical model worked out in detail shows the power of generalized maximum entropy approaches in dealing with cases where strong system independence does not hold. We conclude with practical guidelines for choosing an entropy measure and reporting results so that analyses remain transparent and reproducible.

Photonic interconnects are a key technology for scaling up atomic based quantum computers. By facilitating the connection of multiple systems, high-performance modular quantum processing units may be constructed to perform deeper and more useful algorithms. Most previous implementations of photonic interconnects in trapped ions utilize the scheme of preparing a state, exciting it, and collecting single photons from decays of the excited state. State preparation is responsible for the vast majority of the total attempt time, often taking hundreds of nanoseconds to several microseconds. Here, we describe and analyze a novel technique called “electron juggling" to speed up photonic interconnects by reducing the state preparation step substantially. Using a theoretical framework, we illustrate how this scheme can significantly increase remote entanglement generation rates, approaching the atomic physics limit of the attempt rate in trapped-ion photonic interconnects. Our results indicate that this scheme holds the possibility of achieving remote entanglement generation rates of over 1,000 Bell pairs per second.

Using a custom-built scanning system, we generated maps of birefringence on reflection at $\lambda=1064$~nm from single-crystal GaAs/Al$_{0.92}$Ga$_{0.08}$As Bragg reflectors (henceforth “AlGaAs coatings”). Ten coatings were bonded to fused silica substrates and one remained on the epitaxial growth wafer. The average phase difference on reflection between beams polarized along the fast and slow axes of the coating was found to be $\psi = 1.09 \pm 0.18$~mrad, consistent with values observed in high-finesse optical reference cavities using similar AlGaAs coatings. Scans of substrate-transferred coatings with diameters between 18 and 194 millimeters showed birefringence non-uniformity at a median level of $0.1$~mrad. A similar epitaxial multilayer that was not substrate transferred, but remained on the growth wafer, had by far the least birefringence non-uniformity of all mirrors tested at $0.02$~mrad. On the other hand, the average birefringence of the epi-on-wafer coating and substrate-transferred coatings was indistinguishable. Excluding non-uniformity found at the location of crystal and bonding defects, we conclude that the observed non-uniformity was imparted during the substrate transfer process, likely during bonding. Quantifying the impact on the scatter loss in a LIGO-like interferometer, we find that birefringence non-uniformity at the levels seen here is unlikely to have a significant impact on performance. Nonetheless, future efforts will focus on improved process control to minimize and ultimately eliminate the observed non-uniformity.

This article introduces the 3D magnetohydrodynamic (MHD) equilibrium solver MRX, based on relaxation of a magnetic field to a lower energy equilibrium state via admissible variations. We describe the mathematical theory behind this method and discuss what can be transferred to the fully discrete setting of a relaxation code. Our code is designed to address a number of traditional challenges to 3D MHD equilibrium solvers: enforcing physical constraints such as divergence-free magnetic field, fast convergence, handling strongly shaped polar geometries, and controlling topology changes of the magnetic field. Building on the JAX framework, we address questions of computational efficiency on modern computing architectures, user accessibility, and differentiability at each step. The solver is verified on manufactured vacuum solutions and against the vacuum field of a VMEC equilibrium of a quasi-axisymmetric stellarator. Automatic differentiation with respect to the boundary shape is demonstrated by optimizing stellarator shape for quasi-axisymmetry in vacuum. Relaxation is demonstrated on a finite-$\beta$ configuration, where we study $h$ refinement and the targeted formation of magnetic island chains at rational surfaces following an energy criterion. An inexact Newton method enables the computation of high-resolution finite-$\beta$ equilibria with islands and chaos in minutes on a single GPU.

Unsteady flows generated when a body approaches or departs from a granular bed arise in swimming, burrowing, and maneuvering devices. Yet, the threshold for grain motion in such transients remains poorly modeled due to the complexity of the flow. In this study, we report laboratory measurements of the onset of erosion when a rigid circular disk is subjected to a single vertical stroke through quiescent water above a granular bed. The stroke length and travel time were varied independently to determine the critical velocity at which the granular bed is eroded for different minimum distances from the bed. Two erosion mechanisms are observed for disk motion towards the bed: during the stroke, the outward squeezing flow erodes grains near the edge, while after stoppage, the starting vortex or associated secondary vortices impinge on the surface. For motion away from the bed, only the early interaction between the inward suction flow and the nascent vortex entrains grains. The resulting dimensionless thresholds clarify the respective roles of radial flows and vortices in transient, impulsively driven erosion.

In this work, we present the first derivation and implementation of analytic gradient methods for the computation of the atomic axial tensors (AATs) required for simulations of vibrational circular dichroism (VCD) spectra using configuration interaction methods including double (CID) and single and double (CISD) excitations. Our new implementation includes the use of non-canonical perturbed orbitals to improve the numerical stability of the gradients in the presence of orbital near-degeneracies, as well as frozen-core capabilities. We validated our analytic CID and CISD formulations against two new finite-difference approaches. Using this new implementation, we investigated the significance of singly excited determinants and the role of CI-coefficient optimization in VCD simulations by comparisons among Hartree-Fock (HF) theory, second-order Møller-Plesset perturbation (MP2) theory, CID, and CISD theories. For our molecular test set including (P )-hydrogen peroxide, (S )-methyloxirane, (R)-3-chloro-1-butene, (R)-4-methyl-2-oxetanone, and (M )-1,3-dimethylallene we noted sign discrepancies between the HF and MP2 methods compared to that of the new CID and CISD methods for four of the five molecules as well as similar discrepancies between the CID and CISD methods for (M )-1,3-dimethylallene.

Many protein-protein interaction (PPI) networks take place in the fluid yet structured plasma membrane. Lipid domains, sometimes termed rafts, have been implicated in the functioning of various membrane-bound signaling processes. Here, we present a model and a Monte Carlo simulation framework to investigate how changes in the domain size that arise from perturbations to membrane criticality can lead to changes in the rate of interactions among components, leading to altered outcomes. For simple PPI networks, we show that the activity can be highly sensitive to thermodynamic parameters near the critical point of the membrane phase transition. When protein-protein interactions change the partitioning of some components, our system sometimes forms out of equilibrium domains we term pockets, driven by a mixture of thermodynamic interactions and kinetic sorting. More generally, we predict that near the critical point many different PPI networks will have their outcomes depend sensitively on perturbations that influence critical behavior.