Papers

Polarization-encoded quantum communication requires compensation of polarization transformations induced by the optical links. If the compensator is embedded between two channel segments, the transformations before and after the compensator must be treated separately. Moreover, standard three-wave-plate polarization controllers can become non-universal when their retardances deviate from their ideal values. To address these two challenges, we introduce a four-wave plate compensator that synthesizes arbitrary SO(3) polarization transformations over a broad wavelength range, and an eight-Stokes vector protocol that reconstructs the two link-segment Mueller matrices on either side of the compensator. Our experiment reveals that the four-plate sequence suppresses polarization-induced excess quantum bit error rate (QBER) to the sub-percent level at an operating wavelength more than 100 nm from the design wavelength without further optimization. Combined with two auxiliary wavelengths, our scheme tracks the temperature-driven drift of a strongly wavelength-sensitive fiber spool while keeping the excess QBER below 1%. These results support flexible compensator placement and wavelength channel selection, as well as non-interruptive polarization control in wavelength-division-multiplexed quantum optical links.

As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.

For $d\in\{1,2\}$, let $H=-\frac{1}{2}\Delta + V +\xi$ be the random Schrödinger operator on $L^2(\mathbb{R}^d)$ where $\xi$ is a standard Gaussian white noise and $V$ is a deterministic potential with power-law growth at infinity. Using a Feynman-Kac formula for the trace of the Schrödinger semigroup, we give optimal asymptotic upper and lower bounds on the covariance of $\mathrm{Tr}[e^{-sH}]$ and $\mathrm{Tr}[e^{-tH}]$ as $s,t\to0$ through estimates on Brownian bridge local times. These estimates are a significant improvement on previous bounds in the case $d=1$ and are the first of their kind for $d=2$. As an application of these new estimates, we prove a quantitative hyperuniformity-type property and decorrelation rate for the trace as $s,t\to0$.

We classify the symmetry-constrained forms of the Berry curvature dipole $\mathbf{D}$, gyrotropic magnetic tensor $\mathbf{K}$, and magnetoelectric electro-optic (EO) tensor $\mathbf{G}$, which describe metallic optical and EO effects in time-reversal symmetric, noncentrosymmetric metals. We identify 11 space groups (SGs) in which $\mathbf{D}$ and $\mathbf{K}$ vanish by symmetry while $\mathbf{G}$ remains allowed, thereby providing a more direct route to observing the recently predicted magnetoelectric EO effects associated with $\mathbf{G}$. First-principles based calculations confirm that $\mathbf{D}$ and $\mathbf{K}$ vanish for representative materials, while $\mathbf{G}$ remains allowed and tunable via Fermi level shifting. We further show that the choices of SG and experimental configuration provide complementary paths for isolating $\mathbf{G}$-driven EO effects, including cases where $\mathbf{D}$ and $\mathbf{K}$ are also symmetry-allowed. In an oblique-incidence geometry, the $\mathbf{D}$-driven response produces a helicity-even absorption or gain correction, whereas the $\mathbf{G}$-driven response couples the $s$ and $p$ optical sectors and produces a bias-induced circular dichroism with a characteristic $\sin\theta\cos\theta$ angular dependence. This provides a direct experimental route for separating the $\mathbf{D}$- and $\mathbf{G}$-driven EO signatures in noncentrosymmetric metals.

The nonlinear Hall effect (NLHE) enables the generation of a transverse charge current in nonmagnetic materials with broken inversion symmetry while preserving time-reversal symmetry through the Berry curvature dipole (BCD). However, in crystals with $C_{3v}$ symmetry, the threefold rotational symmetry forces the BCD to vanish, thereby suppressing the intrinsic NLHE despite the presence of finite local Berry curvature. Here, using first-principles density functional theory combined with Wannier-based transport calculations, we demonstrate that uniaxial strain induces the NLHE in monolayer Janus AsTeBr by lowering the crystal symmetry from $C_{3v}$ to $C_{1}$ and generating a finite BCD. The resulting anisotropic redistribution of Berry-curvature hotspots produces pronounced nonlinear Hall conductivity and nonlinear Hall current, with the maximum response obtained at 2% tensile strain. To elucidate the accompanying electronic-structure evolution, we further investigate the strain-dependent optical properties through the joint density of states, dielectric function, optical absorption, and reflectance. The optical spectra exhibit a systematic red shift and enhanced low-energy interband transitions, consistent with the strain-induced reconstruction of the electronic structure. Our results establish a microscopic connection between symmetry breaking, Berry-phase geometry, nonlinear Hall transport, and optical response, demonstrating that uniaxial strain provides an effective strategy for tailoring multiple functional properties in Janus two-dimensional materials.

Elasto-viscoplasticity provides a unified way of describing yield-stress fluids which may exhibit both solid-like and fluid-like behavior. In this work, we present a finite strain overstress-type elasto-viscoplastic framework designed to facilitate the incorporation of different yield surfaces. Within this framework, we compare several yield-surface choices and assess the associated challenges. We consider three representative yield surfaces: (i) pressure-independent, (ii) pressure-sensitive frictional and (iii) capped surfaces, corresponding to von Mises, Drucker–Prager, and modified Cam–clay models, respectively. In the case of von Mises, the proposed formulation naturally recovers the well-known Bingham and Herschel–Bulkley rheologies which are characterized by a single critical yield stress. We discuss in detail the singularity of the Drucker–Prager yield surface which requires a special treatment. In particular, we show that the modified Cam–clay model can be used to conveniently circumvent this singularity under the right conditions, retrieving the expected solution of Drucker–Prager. Implemented within a hybrid Eulerian–Lagrangian scheme, the general framework presented here enables efficient simulations of elasto-viscoplastic flows in two or three spatial dimensions, not requiring regularizing the solid-fluid transition nor a separate free-surface treatment. Numerical benchmark simulations illustrate how yield surface geometry affects velocity profiles, plug formation and compressibility.

Large-eddy simulation (LES) turbulence models often fail to capture the effects of chemical heat release and the resulting modulation of turbulence in premixed flames, underscoring the need for a framework that remains accurate across a broad range of physical regimes. We develop an augmented eddy-viscosity closure, based on deep neural networks calibrated jointly with the LES solution using adjoint-based optimization and differentiable programming, ensuring consistency with the governing partial differential equations (PDEs). Several objective functions and training methods are examined, and each model is assessed for its capability to interpolate and extrapolate across a wide range of Damköhler numbers. Relative to the Smagorinsky-model baseline, the best neural network model improves a posteriori errors in the LES primitive variables by 25-50% and in the resolved Reynolds stress and scalar flux by more than 60%. Crucially, the model generalizes across Damköhler number regimes, maintaining stability and accuracy even for out-of-sample conditions. These results demonstrate that PDE-consistent deep learning closures can recover both mean fields and resolved turbulence statistics in LES of turbulent premixed flames and can therefore provide a broadly applicable framework for turbulent combustion modeling.

In 1971 a group from Columbia University detected polarized X rays from the Crab Nebula at about 3 sigma level during a short sounding rocket flight (Novick et al. (1972)). This measurement, later confirmed to high precision (greater than 20 sigma) by the OSO-8 satellite in 1976/77 (Weisskopf et al. (1978)) marks the real beginning of astrophysical X-ray polarimetry. Since then and before the launch of IXPE, surprisingly few additional measurements had been made. X-ray polarimetry is a demanding field of study. To achieve per-cent-level polarization sensitivity, the collection of more than 1E6 photons in a polarization-sensitive detector is required. As most cosmic X-ray sources are much fainter than the Crab, to do a systematic study of the X-ray sky demands a highly-sensitive payload and a dedicated mission. Further, as the expected polarization will vary with location in extended sources, such as supernovae and pulsar wind nebula, it is vital that the polarimeter should be imaging. First proposed in its current form in 2007, and selected for funding in 2017, the Imaging X-ray Polarimetry Explorer (IXPE) fits this bill. In this chapter we describe: the design and construction of the IXPE payload and spacecraft; its testing and launch; commissioning activities; science operations and; a high-level overview of IXPE science to date. Finally, we conclude with the current status of the Observatory (as of 2025) and prospects for continued operations.

We present an eigenmode-free (EF) method-of-lines (MoL) formulation for the fast synthesis of reconfigurable graphene metasurfaces (MS). As the complexity of MSs increases, analysis by full-wave methods becomes challenging. The MoL is a considerably faster semi-analytical method where the electromagnetic equations are solved analytically along the direction perpendicular to the MS layers and numerically on the MS plane, thereby substantially decreasing the degrees of freedom (DoFs). In existing MoL formulations, the eigendecomposition of the system matrix is calculated numerically, which becomes computationally demanding for MSs with larger cross-sections. To overcome this limitation, we introduce an EF MoL that calculates the S-parameter matrix by analytical closed-form expressions. We demonstrate the potential of the EF MoL by analyzing a reconfigurable graphene MS absorber. The EF MoL shows excellent agreement in the absorbance and is two orders of magnitude faster than the finite element method.

Quantitative prediction of rapid-solidification microstructures requires phase field models that represent the velocity dependence of interfacial properties, including solute partitioning, kinetic liquidus response, solute drag, and kinetic undercooling. These response functions control both microsegregation and morphology selection, but are difficult to prescribe accurately in phase field simulations that employ enlarged interfaces for numerical efficiency. We introduce an optimization-based calibration strategy that embeds target sharp-interface response functions into a dilute alloy phase field formulation by treating the interfacial diffusivity interpolation function as a response-matching degree of freedom. The optimized diffusivity functions are obtained from one-dimensional steady-state phase field solutions constrained to reproduce prescribed continuous-growth-model targets for velocity-dependent solute trapping and drag-modified liquidus kinetics. We demonstrate the accuracy and versatility of the calibrated model in dilute Al-Cu by reproducing the prescribed response functions for intermediate solute drag coefficients relevant to rapid solidification. Two-dimensional simulations are carried out over a range of drag coefficients and pulling velocities relevant to additive manufacturing, demonstrating that quantitative changes in the prescribed interface kinetics can systematically shift the morphology from cellular to banded growth. Additionally, we extend the formulation to dilute multicomponent alloys, enabling independent specification of equilibrium partition coefficients and liquidus slopes for multiple solute species. The framework separates the physical specification of nonequilibrium interface kinetics from their diffuse-interface realization and provides a practical route for embedding response functions into quantitative phase field computations.

We demonstrate that in a scalar-tensor theory where dark matter is conformally coupled to a scalar field, a black hole surrounded by a Hernquist dark matter halo can undergo spontaneous scalarization, acquiring a nontrivial scalar field configuration. The astrophysical properties of the dark matter halo directly dictate the quantum-like spectrum of scalar field configurations around the central black hole. Remarkably, the conformal coupling constant becomes discretized and is determined solely by the halo's compactness parameter.

High-contrast imaging demands extremely sensitive wavefront sensing to correct atmospheric effects and surface errors. While the limits of the sensitivity of a wavefront sensor are well known, a practical, robust design that saturates these limits remains elusive. This work further investigates the PIAA-ZWFS (Phase-Induced Amplitude Apodization-Zernike Wavefront Sensor). In previous work, we developed a framework to optimise its design, maximising Fisher information per frame in the presence of phase aberrations. In these proceedings, we study the effect of various manufacturing and alignment errors in the system on the overall performance. We employ both traditional Monte Carlo sampling and a 2nd order expansion using our auto-differentiable simulator. The performance of the PIAA-ZWFS is not significantly degraded by these errors at values typical for manufacturing.

ORB5X is a C++17/Kokkos AI-assisted translation of ORB5, a global electromagnetic gyrokinetic particle-in-cell code for toroidal confined plasmas. The translation preserves the mathematical model and the main numerical algorithms of the Fortran implementation, i.e. a Lagrangian gyrokinetic formulation, marker-particle representation of the distribution functions, finite-element B-spline representation of the fields, Fourier filtering in the angular directions, MPI domain decomposition and cloning, and HDF5 diagnostic output. The main software change is the replacement of Fortran modules and allocatable arrays by typed C++ classes, structs, namespaces, and Kokkos Views and kernels for performance portability across multicore CPU and GPU backends. This paper summarizes the physical models, the numerical scheme, the distributed and shared-memory parallel design, the AI-assisted code translation workflow, and the validation path used to compare ORB5X against the original Fortran reference codebase. The initial tests for ITG, ITPA, and chirping show an agreement of almost machine accuracy between ORB5X and ORB5 for the electrostatic and electromagnetic field components. Furthermore, we demonstrate that with the modern CMake and Kokkos, ORB5X can be easily compiled and efficiently run on personal Linux and Mac laptops, as well as on high-performance computing clusters such as LUMI-G, Daint-ALPS, CINECA, and Discoverer.

We develop a path-integral-based field theory for deriving Breit-type low-energy Hamiltonians for gapped Dirac systems coupled to both vector and axial gauge fields. Treating the mass gap as the large energy scale, we integrate out the high-energy component of the Dirac spinor and obtain a canonical Schrödinger description for the remaining low-energy degrees of freedom. For the ordinary Dirac equation, our method reproduces the conventional Breit Hamiltonian order by order. In the presence of axial gauge fields, however, the resulting Hamiltonian contains additional vector-axial couplings that have no analogue in the traditional electromagnetic case. We illustrate the method using three- and two-dimensional Dirac models and discuss its relevance to gapped Weyl systems, where dynamical axial fields can generate distinctive low-energy transport signatures.

This paper presents a two-dimensional TMz finite-difference time-domain (FDTD) solver based on Yee's scheme for modeling radiation from an infinitely long z-directed line current, with the open region truncated by a Berenger split-field perfectly matched layer (PML). After validating cylindrical-wave propagation and negligible late-time reflections in free space, the solver is applied to three inhomogeneous configurations: (i) diffraction through a one-cell-thick perfectly electrically conducting (PEC) sheet with single and double slits; (ii) scattering from infinitely long PEC cylinders of circular and rectangular cross section; and (iii) scattering from infinitely long dielectric cylinders of varying cross section and permittivity. Beyond qualitative field maps, the diffraction case is characterized quantitatively: a steady-state phasor extracted by a running discrete Fourier transform yields the transmitted intensity, from which the fringe visibility and the far-field pattern are computed and compared against the closed-form Fraunhofer prediction. The single- and double-slit cases are cleanly separated by a visibility that rises from near zero to near unity, and the double-slit interference maxima agree with the grating condition arcsin(m \lambda_0 / d) to within a fraction of a degree. For dielectric cylinders, the field penetrates the obstacle with the expected reduced internal wavelength \lambda_0 / \sqrt{\epsilon_r}, and the scattered field strength grows with permittivity contrast. A reference-subtraction method isolates the scattered field throughout. The results confirm that the FDTD-PML framework accurately captures open-region diffraction and geometry- and material-dependent scattering.

Recent high spatial and temporal resolution extreme-ultraviolet (EUV) imaging observations have revealed that quasi-periodic pulsations (QPPs), a ubiquitous signature of impulsive energy release in solar and stellar flares, are also present in much smaller-scale coronal events known as EUV brightenings. Whether QPPs observed across such disparate spatial and energetic scales share a common physical origin remains an open question. Here we analyse 2,146 EUV brightenings observed with Solar Orbiter/EUI and 300 EUV solar flares observed with SDO/AIA, identifying 185 brightenings and 89 flares exhibiting statistically significant damped QPPs. We show that the relationship between damping time and oscillation period follows a common power-law scaling for EUV brightenings and EUV solar flares, consistent with previously reported X-ray QPPs spanning both solar and stellar flares. The persistence of this scaling over a wide range of energies and scales suggests that QPPs are governed by a common underlying physical mechanism.

Richards' equation for unsaturated water flow is derived from the kinetic theory of the pore-filling distribution g(r,x,t) of a companion paper. It separates two limits that macroscopic theory usually conflates. The spatial limit is purely kinematic: contracting the representative elementary volume (REV) to a point yields the closed continuum kinetic equation (CKE) d_t g + div F = C[g], with F a pore-resolved pre-closure flux and C[g] the occupancy-gated redistribution operator. The dynamics lies in the temporal limit, the subject of this paper: a Chapman-Enskog (CE) reduction of the CKE controlled by the Damkohler number Da (redistribution time over forcing time), the structural analogue of the passage from Boltzmann to Navier-Stokes. The linearized redistribution operator J is self-adjoint and negative semidefinite in the mass inner product, with a one-dimensional kernel fixing a single invariant (water) and hence one macroscopic equation; the CE hierarchy inverts the same J at every order, only the source changing. Four results follow: the equilibrium step defines the retention curve; the linearized water budget plus the inter-REV source set the first-order equation; its solvability is mass conservation, i.e. Richards' equation; and the response function gives the macroscopic flux, identifying the conductivity K as a first-order transport coefficient, the counterpart of viscosity. Mean-field reduction recovers the standard integral formula; the serial-path correction gives Mualem's heterogeneity penalty. A bimodal pore-size distribution opens a gap in the relaxation spectrum; projecting onto the bands and applying CE within each, with cross-band relaxation surviving at O(1), derives the dual- and multiple-permeability models from first principles, the exchange coefficient set by inter-band connectivity. When Da is not small the expansion breaks down and the full CKE is needed.

Wall-modelled large-eddy simulation (WMLES) treats the subgrid-scale (SGS) closure, wall closure and numerical discretization as independent components, although their effects are coupled through the same resolved field. We present a differentiable hybrid neural–CFD framework in which the SGS and wall closures are learned jointly, end-to-end, within a differentiable flow solver, using only low-order statistics as training targets. Each closure is a composed neural operator: a trainable neural network followed by a fixed differentiable layer that preserves the structure of its conventional counterpart, so that the network learns only the functions left undetermined by the conventional form. Because every operation is differentiable, gradients of the training loss are back-propagated through the coupled solver, allowing both neural closures to be optimized consistently against the flow field, rather than fitted offline or in isolation. We demonstrate the framework, denoted Hybrid-Joint, on a zero-pressure-gradient turbulent boundary layer across a posteriori tests spanning Re_\theta = 600–6500, computational domains and mesh resolutions. The model outperforms WMLES baselines, extrapolates to more than four times the highest training Reynolds number, and transfers to grids and domains absent from training. It recovers a logarithmic mean-velocity region, not imposed by the wall closure, and reproduces the resolved energy spectra accurately, although spectral information is excluded from the training objective. Ablation studies show that learning either closure alone is insufficient and that only joint optimization recovers the full set of statistics, confirming that SGS closure, wall closure and discretization are coupled and must be trained jointly. Once trained, the closures are reused without retraining across all cases, so that training cost is amortized over repeated deployment.

We investigate colored $\Delta_T$ noise, i.e., finite-frequency $\Delta_T$ noise, as a probe of edge-mode (EM) transport in quantum Hall and quantum spin Hall systems. Colored $\Delta_T$ noise probes finite-frequency nonequilibrium current fluctuations and dynamical transport properties that are often obscured in DC measurements of conductance and noise. Since $\Delta_T$ noise is driven solely by a temperature and voltage bias under zero average charge current conditions, it eliminates current-induced Joule heating and directly probes intrinsic thermal fluctuations. We show that chiral, spin-conserving helical, and spin-flip helical (trivial) EMs exhibit distinct colored $\Delta_T$-noise signatures under appropriate bias protocols. Incorporating energy-dependent scattering through a quantum point contact, we demonstrate that electron-hole asymmetry significantly modifies the finite-frequency spectrum while preserving these distinguishing features. Notably, colored $\Delta_T$ noise exhibits a frequency-dependent sign reversal absent in the corresponding white ($\omega=0$) $\Delta_T$ noise. We further investigate zero-temperature colored quantum shot noise and find that it vanishes identically for chiral EMs, whereas the spin-conserving helical response changes sign with frequency. By contrast, spin-flip helical (trivial) EMs exhibit a positive colored shot-noise spectrum. However, the corresponding colored $\Delta_T$ noise retains its characteristic sign reversal, providing a robust distinction between spin-conserving helical and spin-flip helical (trivial) EM transport. These results establish colored $\Delta_T$ noise as a robust, experimentally accessible, complementary probe for identifying chiral, spin-conserving helical, and spin-flip helical (trivial) EM transport in mesoscopic topological systems.

Designing effective Lean proof agents is a central challenge in formal mathematical reasoning. Beyond building stronger provers, recent work emphasizes the workflow around Lean: how an agent decomposes proof obligations, uses tools and compiler feedback, diagnoses failures, repairs proofs, and maintains structured proof context. Motivated by code-level self-evolving agents, we study whether such workflows can be evolved rather than hand-designed. We present a self-evolving Lean proof agent in which a small fixed, trusted runtime wraps a fully mutable workspace: the proof workflow, prompts, and tools. Unlike most self-evolving systems, which optimize against a fixed external benchmark, our system coevolves the agent and its benchmark. Between generations, the highest-scoring agent (the champion) revises the active task distribution through a mastery-throttled curriculum update that introduces harder proof obligations only after the current level is mastered, and a single-anchor recalibration re-runs the champion on the updated benchmark to keep scores comparable as difficulty rises. All evolution stays inside a Lean-grounded verification loop: however the agent rewrites itself, a success counts only when its behavior yields Lean-verified proofs under a trusted snapshot, and each attempt must emit a machine-readable, Lean-grounded proof context whose representation may evolve but whose groundedness is enforced. We run the coevolving trajectory and a fixed-benchmark baseline for 15 active generations and compare them on a held-out miniF2F test split. The best coevolving agent reaches a 45.1% held-out solve rate, versus 12.7% for the seed and 32.0% for the best fixed-benchmark agent, showing that verifier-grounded self-evolution can improve Lean proof workflows under a coevolving benchmark.

The importance of massive stars cannot be overstated: they are powerful probes of the early universe, play a vital role in the chemical and mechanical evolution of their host environments and their end products allow us to study the most extreme physics in the universe. Obtaining accurate stellar and surface parameters for large samples of massive stars is vital to our understanding of how they evolve, and how their births, lives and deaths affect their surroundings. With the large volume of data expected from upcoming spectroscopic surveys, computational limitations will likely be the most important bottleneck impeding our progress. To address this and dramatically decrease computing times, we aim to develop a robust emulator for the FASTWIND radiative transfer and spectral synthesis code. Additionally, we aim to explore alternative fitting methods that have not been feasible until now due to computational costs. We calculate a set of FASTWIND synthetic spectra of OB-type stars, and we train a collection of neural networks to emulate these models. We also develop the open-source python package SpecFANN, which provides users with a suite of fitting methods that can be used with these or other user-generated neural networks. The majority of the trained neural networks reach average accuracies of better than ~0.01-0.1% for photospheric lines and better than ~0.1-1% for wind lines. SpecFANN is able to obtain robust and accurate stellar parameters that are consistent with the literature for a sample of 52 early-type stars. Using SpecFANN we find that we can achieve the same fit in ~1/360,000 of the time when compared to alternative techniques that rely on on-the-fly FASTWIND computations. We have demonstrated that neural networks offer a viable path forward to address the computational limitations of our current atmosphere analysis and stellar parameter determination methods for hot stars.

We employ machine learning techniques to estimate the amount of information contained in three Krylov-space observables: the spread complexity $C(t)$, the discrete Wigner negativity $N(t)$, and the normalized negativity $\chi(t)=N(t)/|S(t)|$, where $S(t)$ is the survival amplitude. We study thermofield-double evolutions on GUE, GOE and Poisson spectra, and on the integrable $SL(2,R)$ Krylov chain of a two-dimensional CFT primary, with Lanczos coefficients $b_n=\alpha\sqrt{n(n+2h-1)}$. To introduce chaos we add a GUE random matrix to the integrable chain, $H(\varepsilon)=H_{SL(2,R)}+\varepsilon R_0 W_{GUE}$, where $R_0$ is its spectral half-width. Either $C(t)$ or $N(t)$ alone determines the thermofield temperature, with $R^2\simeq0.999$. However, neither can reconstruct the fine structure of the spectral form factor. In the integrable sector all three observables carry the same information. When we turn on chaos an asymmetry appears: $\chi$ predicts $C$ better than $C$ predicts $\chi$. This gap grows from $+0.33$ to $+0.77$, while the same gap for the raw negativity decays to zero. The extra information comes from the survival amplitude, and $\chi(t)$ carries it while $C(t)$ can still be recovered from $\chi(t)$. We derive a lower bound on this gap, set by the fraction of the variance of $\chi$ that comes from $S$.

Primordial black holes (PBHs) are not only considered as a candidate for dark matter, but also as potential sources of gravitational waves from binary black hole mergers by the LIGO-Virgo-KAGRA and as seeds for the supermassive black holes observed by the James-Webb Space Telescope, thereby remaining intense interest in cosmology and astrophysics. Fast radio bursts (FRBs) are bright millisecond-duration radio transients whose physical origin remains elusive, which have rapidly developed into one of the most active and rapidly evolving fields in astronomy. The microlensing effect of FRBs offers a clean and powerful probe of PBHs, especially in the mass range above stellar-mass window. In this work, we derive a complete transformation that converts any upper limit on the abundance of PBHs originally derived for `bare' PBHs with monochromatic mass distribution, into the corresponding constraint on `dressed' PBHs with arbitrary extended mass distributions. Based on this framework, we estimate the future constraints on the dressed PBH abundance \(f_{\mathrm{PBH}}\) from FRB observations assuming an expected sample of \(10^5\) FRBs accumulated over the next decade well within the projected detection capabilities of SKA. Our results indicate that including halo enhancement tightens the upper limits on \(f_{\mathrm{PBH}}\) by approximately one order of magnitude, with the most stringent constraint reaching \(\sim10^{-4}\) for the typical mass range from stellar-mass to intermediate-mass black holes.

Quasicrystals exhibit long-range positional order without periodicity, arising from multiple incommensurate wave vectors. In self-assembled quasicrystals, the spontaneous breaking of translational invariance gives rise to two Goldstone modes-phonons and phasons-associated with two competing wave vectors. Here, we demonstrate a unique scenario exclusive to quasicrystals: a mechanism that gaps out only one Goldstone mode (associated with one wave vector), while the other remains gapless. In one dimension, this leads to the coexistence of long-range order at the gapped wave vector and quasi-long-range order at the gapless one, due to sustained fluctuations. We show that this phenomenon can be realized using ultracold bosonic atoms in optical cavities.

This study examines phonon-mediated heat transfer across Si/SiC and SiC/diamond interfaces using non-equilibrium molecular dynamics simulations, emphasizing the influence of SiC polytypes and amorphous interlayers. For sharp interfaces, 4H-SiC exhibits considerably higher interfacial thermal conductance (ITC) than 3C-SiC, due to its broader active phonon spectrum and superior spectral matching with Si. While amorphous layers generally reduce ITC, a key observation is that an ultrathin 0.5-nm amorphous SiC (aSiC) layer can enhance heat transport in the Si/3C-SiC system: the ITC increases from 613 MW/m^2-K (sharp) to 716 MW/m^2-K, demonstrating a phonon-bridge effect. VDOS (vibrational density of states) analysis confirms that optimized ultrathin aSiC layers improve vibrational overlap and open additional phonon-transport channels. In contrast, thicker or silicon-rich amorphous layers significantly suppress ITC through enhanced inelastic phonon scattering. For SiC/diamond interfaces, any amorphous layer, particularly aSi, causes severe ITC degradation, highlighting the need for sharp, defect-free bonding to exploit diamond's high thermal conductivity.

Quantum machine learning models define probabilistic input–output maps through coherent quantum evolution and measurement. While such models can exhibit computational advantages, their internal functioning and decision making generally resists interpretation in terms of stochastic trajectories through intermediate configurations. In contrast to classical (Markovian) stochastic processes, quantum dynamics generically violates the Chapman–Kolmogorov divisibility condition, preventing a decomposition into probabilistically meaningful intermediate transitions. We develop a probabilistic framework for representing quantum learning models as stochastic processes over configuration spaces where the dynamics are modeled as linear maps on probability distributions. Starting from a fixed POVM, arbitrary quantum channels induce transition kernels on the associated probability representation. For informationally complete POVMs, and in particular SIC-POVMs, these kernels are Markovian but generally quasi-stochastic, with non-classicality appearing as negativity. By contrast, projective spaces admit positive stochastic kernels but generally require non-Markovian dynamics due to the failure of Chapman–Kolmogorov divisibility. This yields a trade-off between negativity and dependence on past configurations, i.e. quantum dynamics can be represented either by Markovian quasi-stochastic maps or by positive stochastic processes with higher Markov order. We discuss how such representations of quantum dynamics can be interpreted as stochastic walks through a memory space in the spirit of Projective Simulation, a model of learning and agency in which decisions arise from random walks over an episodic memory network. We further outline how finite-order stochastic kernels can approximate such quantum deliberation processes and show in what regimes the classical machine learning model is recovered.

Preserving the complex visibility of coherently captured starlight is essential for quantum-assisted long-baseline interferometry, because this nonlocal coherence carries the spatial information needed to form astronomical images. Yet finite memory lifetime and imperfect retrieval inevitably produce storage failures; when a failed cell is heralded, its erased subsystem can leak which-node information to the environment and dephase the stored coherence. Strict finite-dimensional (\(U(1)\)) covariance further forbids uniform exact correction of all single-memory erasures. We address this combined physical and symmetry-imposed limit using local number-conserving quantum scrambling, parameter-independent pattern-conditioned recovery, and a fixed Gottesman–Jennewein–Croke receiver. We prove a channel-to-Fisher-information stability theorem that converts approximate logical recovery into an operational guarantee on receiver-accessible information over compact visibility regions. All-pattern finite-size simulations show that scrambling redistributes erasure risk and suppresses high-leakage events, while a separate equal-budget comparison identifies a shallow design that outperforms the tested deeper and charge-sector Haar-random benchmarks at the prespecified operating point. A separate compilation resolves every nontrivial recovery branch into abstract nearest-neighbor number-conserving gates. Although the present low-rate design does not yet improve fixed-total-memory throughput, it establishes a blueprint for converting spare memory capacity into protection of astronomical coherence, opening a path toward higher-rate, erasure-resilient quantum telescope architectures.

Driven-dissipative quantum emitters provide a powerful platform for controllable excitation storage and release, with promising applications in quantum batteries and quantum storage. Yet, transient excitation transfer in collective many-body systems is often obscured by the intricate interplay among coherent driving, dissipation, and correlation dynamics. Here, we uncover a scalable excitation-storage mechanism in two emitter ensembles coupled to a semi-infinite waveguide. A coherently driven ensemble acts as an effective excitation reservoir, while a second ensemble positioned near a dissipative node serves as a subradiant storage medium. Surprisingly, when the driven ensemble largely exceeds the storage ensemble in size, the transfer dynamics enters a nearly correlation-free regime, allowing the driven ensemble to behave effectively as a classical excitation source. This reveals a simple scaling law for optimal excitation transfer, under which the storage ensemble approaches near-complete population inversion as the driven ensemble size increases. Building on this mechanism, we propose a three-stage storage-and-release protocol enabling fast excitation storage and controllable photon emission. Our results demonstrate how coherent and dissipative collective interactions can be jointly harnessed for quantum energy storage and programmable nonequilibrium dynamics in waveguide QED platforms.

Suppose that we encode quantum information into $ n $ physical carriers. With what probabilities can we perfectly recover the quantum information after sets of carriers have been erased? This question arises in the study of quantum erasure channels, for which M.B. Hastings conjectured that if each carrier is independently erased with probability $ t \geq 1/2 $, then it is impossible to recover the quantum information with asymptotically vanishing error and probability $ > 1-t $. We prove this conjecture by establishing new no-cloning constraints on realizable recovery probabilities.

The transcorrelated (TC) method dramatically accelerates the convergence of correlated calculations toward the complete-basis-set (CBS) limit by folding a Jastrow correlator into the Hamiltonian via a similarity transformation, incorporating the electron–electron cusp into the effective interaction. We make the TC framework practical for large systems and flexible, multi-center correlators by compressing the grid-evaluated TC integrals with the interpolative separable density-fitting (ISDF) approximation, combined with the effective two-body (xTC) treatment of the three-body operator. This low-rank representation reduces storage and integration costs by orders of magnitude, and a multi-GPU implementation with automatic differentiation of the correlator makes the construction routine for large basis sets. We demonstrate the resulting ISDF-xTC-CCSD method on the linear hydrogen chain, reaching the joint thermodynamic and CBS limits with basis sets up to cc-pV5Z in agreement with state-of-the-art many-body references to within about 1~mHa/atom, and on the benzene ground-state energy with up to 1200 orbitals (cc-pCV5Z), where the method attains state-of-the-art accuracy at the coupled cluster singles and doubles level and its CBS extrapolation is markedly more robust than that of conventional coupled-cluster methods.