Papers

We derive a thermodynamically-consistent model of fluid flow through a poroelastic medium. Starting from elastic and fluid free-energy densities, an energy-dissipation rate, and a kinematic constraint, the force-balance equations are derived using variational principles, with the pressure–density constitutive relation emerging as a direct consequence of the variational structure; the same kinematic constraint also supplies the total-flux transport structure. In the ideal-gas limit, the model linearization recovers the classical linear Biot equations. For power-law fluid energies, it yields isentropic pressure–density relations. A key advantage of the variational formulation is that extensions to richer physics, such as thermal effects, chemical reactions, or multi-component fluids, can be incorporated systematically by augmenting the energy and dissipation functionals without redesigning the force-balance or transport closure. We support the model with an energy-compatible two-field discretization and study consolidation under a surface load with three lateral-boundary treatments and three fluid-compressibility exponents.

Matrix product operator (MPO) serves as a fundamental component in tensor network simulations of quantum many-body dynamics. We employ an MPO construction that introduces additional propagation channels to embed both periodic boundary conditions and finite-range couplings directly into an open boundary MPO. We apply this construction within the time-dependent variational principle (TDVP) framework to simulate quench dynamics in a spin-1/2 chain with finite-range interactions, and benchmark the results numerically against the fourth-order Runge-Kutta method, finding excellent agreement for both single-body and two-body observables. The approach offers a practical route for tensor network simulations of many-body dynamics in periodic finite-range systems.

Spin space groups provide the natural symmetry framework for magnetic crystals with weak spin-orbit coupling, but extracting their physical consequences requires a general theory of irreducible representations. The central difficulty is that spin space groups are represented projectively: their factor systems can render lattice translations noncommuting, induce nonsymmorphic actions in momentum space, and modify the projective structure of little cogroups. These effects lie beyond conventional space-group and double-group representation theory. Here, using Mackey's theory of group extensions, we develop a unified constructive framework for all collinear, coplanar, and noncoplanar spin space groups, including antiunitary symmetries and general factor systems. A central technical result is a canonical decomposition of the relevant factor system $\nu$ into a translational factor $\sigma$, a mixed factor $\gamma$ coupling translations to point-group operations, and a point-group factor $\alpha$. This decomposition makes transparent how ordinary representation theory is modified: $\sigma$ determines the projective translation algebra and the appropriate Brillouin zone, $\gamma$ controls the momentum-space group action and can make it nonsymmorphic, and $\alpha$ contributes to the factor systems of little cogroups. On this basis, we construct all projective irreducible corepresentations by induction over momentum-space orbits. The framework identifies which nonsymmorphic momentum-space symmetries can be realized by spin space groups and reveals Brillouin spaces that are compact flat manifolds rather than tori, symmetry-enforced Zak phases, reconstructed high-symmetry momenta and band degeneracies, and new types of quasiparticles. Our results establish the representation-theoretic foundation for systematic studies of weak-spin-orbit-coupled magnetic materials.

Microquasars are candidate sites for high energy particle production within our galaxy. Because their distances and compact emission regions limit direct observational constraints, numerical simulations are useful for connecting jet dynamics with possible multi messenger signatures. This work models neutrino production from relativistic magnetohydrodynamic microquasar jets, focusing on the proton photon channel associated with interactions between accelerated protons and ambient photon fields. Proton proton interactions are acknowledged as a possible process in dense environments, but they are not modeled in the present calculation. A ray tracing procedure is applied to the hydrodynamic simulation output to produce synthetic neutrino images from the perspective of a stationary observer. Synthetic spectra and intensity maps are presented and compared with representative sensitivities of current and future neutrino detectors.

Hybrid key establishment runs a post-quantum key-encapsulation mechanism (KEM) alongside a classical Diffie-Hellman primitive, so that the session key stays secure while either component resists attack. This design is now standardized in the Transport Layer Security protocol, Secure Shell, and the Internet Key Exchange, with the standardized module-lattice KEM (ML-KEM) as the post-quantum component. A hybrid KEM secures the derived key, but not the integrity of the negotiation that selects which primitives are used. Full protocols authenticate that negotiation through a handshake transcript; a hybrid KEM deployed as a standalone drop-in primitive, or inside a minimal handshake without transcript authentication, inherits no such guarantee, and an active attacker can strip the post-quantum option. We ask what the key schedule alone must contain to make downgrade resilience a local property of the combiner. We give a game-based definition at the combiner layer and prove a two-sided separation: a combiner that ignores the transcript is downgraded with certainty, whereas one that binds the session key and the confirmation tag to a hash of the transcript blocks every such attempt, up to a term negligible for a 256-bit transcript hash. We also give an explicit strongest-link security bound. Using a calibrated cost model composed from published Cortex-M4 measurements, transcript binding adds one hash per party - about 11.8% of handshake computation but only 1.5% of radio-inclusive energy - and adds no messages or bytes on the wire. Every reported number is produced by a released harness that passes a 30-check validation gate.

Pair density Shannon entropies and mutual information in position and in momentum space are calculated for two series of homonuclear and heteronuclear diatomic molecules using Hartree-Fock wave functions. The pair density entropy sum increases with the quality of the basis set. Mutual information, a measure of statistical correlation, is seen to be smaller in momentum space as compared to position space. The interpretation is that the momentum pair density is closer than the position pair density to a Hartree-like reference. The $N$-dependent behaviours of the entropy and mutual information sums are examined by fitting the data to three different model behaviours. Results show that these models are capable of representing the molecular data in differing degrees. The parameters obtained from a particular model are relatively constant across different chemical series. This adds evidence to the argument of a universal $N$-dependent behaviour of the entropy sums.

Baryon-meson sum rules enable cross-checks among exclusive mesonic and baryonic measurements. Unlike their heavy-to-heavy counterparts, analogous heavy-to-light relations lack an explicit heavy-quark-symmetry origin. We investigate their origin in the $b\to s\nu\bar\nu$ system by studying the relevant helicity amplitudes at the two kinematic endpoints. Although the sum rule follows algebraically from the simple short-distance structure, its coefficient depends nontrivially on the hadronic dynamics. At maximum recoil, the coefficient for the daughter baryons ${\cal B}=\Lambda,\Xi$ is consistent with the approximate value $1/2$ within current form-factor uncertainties. At zero recoil, by contrast, a kinematic helicity projection enforces its exact vanishing. The fully integrated coefficients are numerically close to $1/4$. However, unlike the one-quarter coefficient in the heavy-to-heavy system, they are not local symmetry coefficients but integrated quantities shaped by the behavior between the two endpoints. This understanding may guide the construction of baryon-meson sum rules in more general heavy-to-light decays.

Here we report on the status of the first on-orbit technology demonstration of the Little UV Camera (LUVCam1). LUVCam1 is a 0.4U payload aboard the 2U cubesat GRBBeta that was launched in July 2024. The primary objective of the mission was to qualify the GSENSE4040 CMOS sensor and its readout electronics in preparation for the Quick UltraVIolet Kilonova Surveyor (QUVIK; Werner et al. \cite{Werner22, Werner24}) satellite. LUVCam will serve as the primary scientific camera for this mission. We report on the performance of the GSENSE4040FSI sensor used on LUVCam1, beginning with its in-lab characterization followed by an assessment of its on-orbit performance using astronomical observations made by LUVCam1. The camera remains operational two years later and has taken pictures of the Moon and stars that we show here. Through LUVCam1, we demonstrated an approach leveraging new-space to qualify the latest COTS CMOS sensors for astronomy missions.

We show how the Pinnacle architecture can be adapted to be compatible with superconducting qubits, where the connectivity between qubits must be fixed at fabrication. Specifically, we present a modified instantiation of the architecture that has fixed connectivity degree of eight. With this instantiation, we show that a 2048-bit RSA integer can be factored in one month with approximately 120 000 physical qubits, given a physical error rate of $10^{-3}$, code cycle time of 1 microsecond and reaction time of 10 microseconds.

Polarizability predicts how an object responds to an incident electromagnetic field, the interactions between small particles, or the optical forces exerted upon them. Polarizability is responsible for the effective-medium properties of artificial materials or metasurfaces. Despite significant progress in all these areas, it is unclear what the limits of the strength of such interactions are or, more specifically, what the upper bounds on the polarizability of a given spatial region that an unknown and designed particle would occupy are. This work connects the electromagnetic field description via an integral equation with a dual formulation of quadratic programming to derive fundamental bounds on components of all four polarizability tensors or on their specific combinations. In particular, the work establishes an intuitive visualization of what strong polarizability means and how strong it can be. The developed fundamental bound also answers which materials and domains are best for the given demands on polarizability. These findings establish a versatile platform that can accommodate a wide range of demands on polarizable bodies, providing an absolute measure of their performance against which the results of human-powered or automated design procedures can be compared.

Barren plateau diagnostics characterize whether gradient signal remains available for training, but surviving signal need not translate into successful optimization. We study this trainability–optimization gap at the level of optimizer steps. Treating coefficient-weighted Hamiltonian-term gradients as task-like components, we introduce step-level diagnostics and derive an exact bridge between signed termwise organization, directional activity, and first-order descent. Resolving this bridge into standard first-order geometry shows that the apparent organization–activity factors are not independent optimization axes and that, at fixed state and update norm, the raw gradient maximizes first-order descent of the summed objective. We compare vanilla gradient descent, a deterministic Hamiltonian-term PCGrad variant, and probe-gated LSO-PCGrad on transverse-field Ising model instances with hardware-efficient and Hamiltonian variational ansatzes, together with matched controls for update norm and probe budget. Blind projection can improve an organization diagnostic while worsening final energy and first-order predictability. After conditioning on standard first-order geometry, residual term-space composition shows no reproducible material incremental association with realized descent, while optimizer-relative update norm shows positive material associations in some settings without cross-regime reproducibility. Matched controls provide no resolved final-energy benefit attributable to the projected direction, and the improvement of LSO-PCGrad is more consistent with probe-based search and step-norm adaptation than with Hamiltonian-term projection itself. These results show that gradient-structure diagnostics can characterize trainability and update geometry without serving as standalone evidence of optimization benefit, which requires controls matched on update norm and search budget.

We introduce a relation problem constructed from the Clauser–Horne–Shimony–Holt (CHSH) game, which we call the two-round one-dimensional CHSH problem. Its two-round structure ensures that the CHSH questions are supplied only after the relevant Pauli-frame data have been fixed, thereby ruling out a simple classical strategy that solves the corresponding problem perfectly when all inputs are supplied simultaneously. We construct a quantum circuit on $2N$ qubits that uses only adjacent two-qubit gates, has operational depth at most eight, and achieves the optimal quantum success probability of CHSH, which is strictly smaller than one. We prove that, for every fixed $0\leq\delta<(\sqrt{2}-1)/4$, any randomized classical circuit with fixed wiring and bounded gate fan-in that achieves an average success probability of at least $(2+\sqrt{2})/4-\delta$ requires depth $\Omega(\log N)$ after the questions of the second round are supplied. This yields an unconditional separation even though the quantum circuit is restricted to a one-dimensional geometry, whereas the classical circuit has no geometric locality restriction. The result shows that perfect quantum success is not necessary for unconditional quantum advantage with shallow circuits.

At high Peclet number, the classical Taylor-Aris dispersion model becomes inadequate for early-time contrast-agent transport in arteries, while an explicit closure model and a clear physical interpretation of this regime remain lacking. In this study, we develop a novel explicit-closure one-dimensional (1-D) effective dispersion model for this regime, with its functional structures identified through symbolic regression. Analysis of the resulting model reveals that, in the high-Peclet-number regime, axial transport is redistributed between the effective convection flux and the dispersive flux, resulting in a reduction of the effective convective transport velocity in the dispersion model. This redistribution gives rise to a transition from the classical quadratic scaling to a linear scaling of the effective diffusivity with radial Peclet number. Numerical validation demonstrates close agreement with the convection-diffusion model over the investigated high-Peclet-number conditions, while the classical Taylor-Aris model exhibits substantial deviations. Application of the proposed model to averaged flow velocity inversion further demonstrates improved velocity estimation, particularly in the high-Peclet-number regime. These results highlight the importance of accounting for non-classical dispersion for reliable contrast-agent-based arterial blood flow velocimetry and provide new insight into high-Peclet-number mass transport.

In this paper, we completely resolve the two total positivity problems raised by Li [J. High Energy Phys., 2023(7):Paper No. 167, 44 pp., 2023], thereby determining the precise scope of this positivity structure in the one-dimensional conformal bootstrap. First, the Gauss hypergeometric kernel $\mathcal{F}(\Delta,z) = {}_2F_1(\Delta,\Delta;2\Delta;z)$ is proved to be strictly totally positive of order infinity for $\Delta > 0$ and $z \in (0,1)$. Second, the sharp lower $\Delta$-parameter threshold for the associated one-dimensional $SL(2,\mathbb{R})$ conformal block $G_{\Delta}(z) = z^{\Delta}\mathcal{F}(\Delta,z)$ is shown to be $1/2$: the conformal-block kernel $G_{\Delta}(z)$ is strictly totally positive of order infinity for $\Delta \geq 1/2$. For every $\tau \in (0,1/2)$, there exists a strictly negative odd-order minor of $G_{\Delta}(z)$ such that all its $\Delta$-values are in $(\tau,1/2)$ and all its $z$-values can be chosen arbitrarily close to one. Consequently, no restriction $z > z_0$ with $z_0 < 1$ can restore total positivity of order infinity over all $\Delta > 0$.

High-dimensional bipartite entanglement depends on how probability is distributed across the Schmidt spectrum, whereas a single reference-state fidelity resolves only one spectral scale. We develop multiscale Schmidt-spectrum bounds that connect geometric measures with quantitative Schmidt-number witnesses. For any partition of a pure-state Schmidt spectrum, several nested Vidal tails determine block masses. We derive the sharp upper boundary of the associated normalized nuclear-norm coordinate and show that equality holds if and only if the spectrum is uniform within each block. Refining the partition gives a monotone hierarchy of tighter bounds whenever the added tail data distinguish unequal block means. We also solve a relaxed weighted multiscale optimization globally: one scalar parameter specifies its unique full-support optimizer and explicit value on the nontrivial branch. Using the established single-tail fidelity–resource curve as a baseline, we obtain exact-fidelity equality refinements, convex-roof lower bounds for mixed states, and quantitative calibrations of Schmidt-number witnesses. A higher-tail relation further bounds convex-roof extended negativity in terms of Vidal tails and identifies the pure-state equality spectra. Leakage-aware and joint-confidence formulations state how these bounds can be used with incomplete data. Multistep tails require block-resolved or independently certified spectral information; they are not determined by one projector expectation.

Soft materials linked by reversible covalent or supramolecular bonds form a diverse class of assemblies with promising applications from nanoscience to medicine. Experiments typically probe bulk phase behavior and rheology, but it remains difficult to measure how microscopic bonding kinetics and the mechanics of the constituent elements give rise to bulk properties. Coarse-grained molecular dynamics (MD) simulations can bridge these scales, but most simulation approaches do not control individual bond kinetics, and those that do were mostly developed for bespoke applications that do not readily generalize. Here we present pySNAP (Simulation and Network Assembly Pipelines), a modular open source Python platform that integrates tunable dynamic bonding with a workflow, template, and analysis setup, so that users can study a wide range of systems with only small changes to input files. The platform is built on the GPU-accelerated HOOMD-blue MD engine and integrates DyBond, a GPU-accelerated plugin that forms and breaks bonds consistent with an equilibrium distribution and supports bonding between multiple types of partner species. Around this core, the snap_simulate package compiles a directory of parameter files into a HOOMD-blue simulation, and the snap_workflow package orchestrates the resulting parameter sweeps across workstations and high-performance computing schedulers. We describe the theory behind simulated dynamic bonding and how to use the package, from setting up a parameter sweep to analyzing its results, and demonstrate the framework on a diverse range of dynamically bonded systems, showing that it accommodates distinct interaction mechanisms, geometries, and physical scenarios within a unified workflow, while enabling both reproduction of existing models and rapid construction of more complex composite systems.

We present a neural network (NN) basis-selection method for large-scale relativistic configuration interaction (RCI) calculations in GRASPG. The method employs configuration state function generators (CSFGs), each of which generates a set of configuration state functions (CSFs) with the same spin-angular couplings, as the basic selection units for the NN. A constant-orbital feature-elimination strategy removes feature channels whose values remain unchanged across the CSFG pool. The CSFG representation reduces the number of learning units processed by the NN by more than one order of magnitude, while constant-orbital feature elimination further reduces the dimensionality of the NN input. Combined with the high-performance GRASPG framework, the method improves the efficiency of both NN selection and subsequent RCI calculations, maintaining a balance between accuracy and computational cost. In a moderate Ni(12+) benchmark, where the corresponding full-space RCI calculation is still feasible, the CSFs generated by the retained CSFG sets reproduce the full-space RCI results at the few inverse-centimeter level for the target states. For the representative J = 0, even-parity block, the complete workflow reduces the wall time by 75.6 percent, and the peak memory required by a single RCI calculation is reduced by a factor of 10.1. In a larger-scale calculation with a full CSF expansion containing 1.27 x 10^9 CSFs, the method retains only 1.1-1.9 percent of the full-space CSFs and yields energy levels in good agreement with experimental data and other resource-intensive theoretical calculations.

Phase-field models provide a versatile framework for simulating fracture nucleation and propagation without explicit crack tracking. Their principal computational challenge is the need to resolve a small regularization length with a sufficiently fine finite element mesh. This requirement can render uniform-mesh simulations prohibitively expensive, particularly for three-dimensional problems, problems involving distributed crack nucleation, and soft nearly incompressible materials. Adaptive mesh refinement offers a natural means of reducing this cost, but existing approaches often rely on heuristic refinement indicators, are primarily designed for problems containing pre-existing cracks, and retain increasingly large refined regions as cracks grow in size. This work presents an adaptive mesh coarsening framework along with adaptive mesh refinement for phase-field fracture. The coarsening strategy replaces the fractured region in the crack wake with a coarse crack band that preserves the essential mechanical behavior of a crack, making it necessary to have a refined mesh only in a small region near the tip of the growing crack. The method introduces a physics-based refinement indicator derived from the violation of the material strength surface, which is a necessary condition for fracture evolution. The indicator therefore robustly identifies regions where crack nucleation or propagation is imminent and can be applied across arbitrary materials, geometries, and loading conditions. The framework is implemented in parallel within FEniCSx; the supporting finite element codes are made available. Its generality and substantial computational benefit are demonstrated through benchmark problems involving crack propagation and nucleation under quasi-static and dynamic loading, for soft and hard materials, and for thermomechanical fracture.

Chirality-induced spin selectivity (CISS) is usually associated with molecular handedness. The possible contribution of chiral phonons is less established. We study a helical tight-binding model in which local phonon angular momentum modulates spin-dependent nearest-neighbor hopping. Fewest-switches surface hopping calculations give the transmitted spin polarization $\mathrm{SP}=aC+b\mathrm{PH}$. Here $C$ is the molecular chirality and $\mathrm{PH}$ is the phonon chirality. A mirror symmetry reverses $C$, $\mathrm{PH}$, and $\mathrm{SP}$ simultaneously. This symmetry excludes both a chirality-independent offset and a $C\cdot\mathrm{PH}$ term. The phonon contribution can therefore enhance, cancel, or reverse the molecular CISS signal.

The scotogenic model offers an attractive framework that explains light neutrino masses while accommodating a viable dark matter candidate. In this work, we study the phenomenology of singlet fermion dark matter, $N_1$, produced via the freeze-in mechanism characterized by an instantaneous low reheating temperature, $T_{\text{rh}}$. We explore the model's viable parameter space, incorporating current bounds from lepton flavor violation ($\mu \to e \gamma$) and direct detection limits. We show that low-reheating scenarios significantly reshape the viable region for $N_1$ dark matter, improving prospects for testing the scotogenic model in the FIMP scenario while keeping it consistent with current lepton flavor violation and cosmological bounds.

The manuscript provides a description of a theoretical analysis of a non-Newtonian Prandtl fluid subject to peristaltic flow through an inclined asymmetric channel. We explore the effect of an inclined magnetic field on the peristaltic flow. This is relevant for applications involving fluid flow in narrow, inclined (tilted) tubes similar to blood vessels or the digestive system. The model also includes thermodynamic aspects such as heat diffusion (the Soret effect) and viscous dissipation resulting from wall-fluid slip conditions, which may help optimize medical devices such as lab-on-a-chip systems and dialysis machines. In this study, the concentration of a generic chemical, temperature, and fluid velocity are taken into account through mass, heat, and momentum balances, respectively. The solution is approximated using numerical techniques suitable for long wavelengths (low frequency) and low Reynolds numbers. The study also discusses trapping phenomena, which are crucial from a clinical point of view. The developed insights can improve the understanding of physiological flows in the gastrointestinal tract and blood vessels. By understanding how the fluid moves and how particles are trapped, these insights may contribute to the design of improved medical pumps and artificial organs. Graphical visualizations are provided for the fluid velocity profile, temperature distribution, and concentration of a generic chemical. Furthermore, the numerical results are validated through comparison with a closed-form solution from a benchmark problem.

In this article, I will define the Chern-Simons invariant of a gauge connection over a three-manifold and explain some of the things that are special about it, in particular its role in understanding the quantum Hall effect, which is an important effect in condensed matter physics, and its generalization, the fractional quantum Hall effect. (The article is based on a lecture at a conference celebrating the 75th birthday of James Simons, held at the CUNY Graduate Center in 2013. It will appear in the Bulletin of the American Math Society.

We present high-time resolution spectro-polarimetric data for 34 new fast radio bursts (FRBs) discovered by the Commensal Real-time Fast Transients (CRAFT) survey on the Australian Square Kilometer Array Pathfinder (ASKAP) during the period May 2024 to June 2026. Most of these were detected by the higher-sensitivity CRAFT COherent (CRACO) detection system that was commissioned on the telescope during this period. This new sample doubles the size of the CRAFT HTR catalogue and probes a fainter population of FRBs thanks to the improved sensitivity of CRACO. We compare the distribution of extragalactic rotation measure (RM) and polarisation fraction to the CHIME and DSA catalogues. While no significant differences were seen between CRAFT and DSA, the extragalactic RM distribution seen in CHIME FRBs (which are detected at lower frequency) was substantially lower. Surprisingly, we find no significant differences in the linear polarisation fraction distribution between the three FRB catalogues, suggesting an indifference to the different telescope observing frequencies. We show tentative evidence for wider and fainter bursts possessing lower polarisation fractions; this is predominantly driven by the growing sample of unpolarised bursts that are, in almost all cases, wider ($\gg$10 ms) and fainter ($\ll$10$^{34}$ ergs s$^{-1}$ Hz$^{-1}$) than the median ASKAP detection.

Estimating a ground-state energy and preparing a ground state both split into a front-end that supplies an approximate state $|\chi\rangle$ and a back-end that reads or refines it. We argue, and measure at every step, that the only thing crossing that split is the front-end's spectral weight distribution $p_n=|\langle E_n|\chi\rangle|^2$ over the eigenstates of the problem Hamiltonian $H_P$. Everything downstream is priced in it, so the front-end is a swappable module, and we swap it seven times without altering anything to its right. We call the arrangement WARP. Four back-ends spend that distribution. A readout (A) samples $g(t)=\langle\chi|e^{-iH_Pt}|\chi\rangle$ on the device and extracts true eigenvalues by classical signal processing, returning an energy below the variational bound of the state it was handed, from a signal $50\times$ shorter than a Fourier transform needs. Three routes spend the same account on a state instead. An adiabatic schedule (B) has a minimum gap set by the overlap; a discrete amplification (C) is cheaper in depth but must halt at an index the overlap fixes, so it needs a measured value and not a bound; a recursion (D) of depth $\mathcal{O}(3^m)$ raises the question of whether it can be run at all. The interactions are the substance. On stretched N$_2$ a sample-based diagonalisation reports a variational bound 79 mHa high, fifty times outside chemical accuracy; reading the state it has already prepared returns $E_0$ to $1.4\times10^{-6}$ Ha, three orders of magnitude inside it, and needs no new front-end and no new circuit. And because the weights price everything, they must be measurable when scarce, so we beat a weak reference against a strong one and recover overlaps four orders of magnitude below the shot-noise floor. We map the operating envelope throughout, and state the limitations.

Entanglement cost is the asymptotic rate of Bell pairs required to prepare a quantum state. Its regularized definition has made exact evaluation difficult, even for isotropic states (i.e., depolarized maximally entangled states). In this work, we determine the entanglement cost of every qubit isotropic state and, more generally, every two-qubit Bell-diagonal state. Our proof uses a family of suitably tuned qubit semigroups to transform a general log-Sobolev entropy bound into supporting lines of Wootters' function. A recent exact tensorization theorem of Dong et al. [arXiv:2606.17729] extends these bounds to arbitrarily many copies, yielding a general lower bound on entanglement cost. Matching this bound with the entanglement of formation also determines the exact cost for a broader class of two-qubit states. For qudit isotropic states, we derive another general lower bound from the full depolarizing $2\to 3$ norm. This bound substantially improves on the PPT-relative entropy of entanglement and nearly matches the entanglement of formation, leaving a gap of at most $2\%$ of $\log d$ that vanishes as $d$ grows. Finally, the exact qubit results and qudit bounds extend to the entanglement cost of preparing quantum depolarizing channels under both parallel and sequential strategies.

Dendritic silhouettes are cheap. Pond ice neurons, diffusion-limited aggregates, wiring-minimizing arbors, and biological neurons can share a branching statistic because they share a growth instability, not because they are functionally similar. We take that observation as a modeling constraint. The ice neuron only structurally resembles a biological neuron. A biological neuron carries an internal state and a field that it helps generate, letting geometry, dynamics, and field co-sculpt one another. In our construction, the ice neuron is the zero-gain limit of an interfacial system in which a FitzHugh–Nagumo state lives on the moving front and writes back into the field that drives it, with dimensionless gain $\gamma$. At $\gamma=0$, morphology and internal dynamics decouple into a Mullins–Sekerka growth band and a damped oscillator. Finite $\gamma$ deforms the spectrum, shifts the selected instability, and opens an oscillatory region below the isolated Hopf threshold. On a dendrite grown at $\gamma=0$ and then frozen, field closure increases distal persistence without changing the geometry; its first-order effect remains positive across repeated realizations of the same growth rule and localizes to a small set of non-cable field shortcuts. Nonlinear dynamics remain finite beyond the linear instability: below threshold, closure is almost the transmission; at spike amplitude, it is a correction to a regenerative cable event. A dendritic tree therefore is not intrinsically a structural spandrel. Shape alone is morphology without an internal state or a source term. That is the dendritic silhouette of the ice neuron. Excitability adds an active cable. Field closure is the biophysical mélange in which the neuron helps write the field that acts back on it, sculpting its dynamics and, during growth, its structure. That is a biological neuron.

Boltzmann counting possesses an exact scale invariance from which the specific entropy emerges without asymptotic approximation. Under thermodynamic replication, this invariance exposes the Gibbs excess at the multinomial level. Its resolution reveals a state-dependent scale structure whose additive refinement generates a continuum reference measure, giving a combinatorial origin to the reference structure of continuous statistical mechanics. The resulting entropy is relative to this measure, and for an ideal gas the scales acquire operational meaning through reversible work.

Electrospray ionic liquid ion sources operating in the pure ion regime emit metastable, low solvation number ion clusters whose post-emission dissociation modifies plume composition, energy distributions, and diagnostic observables. While field-free measurements support first-order, thermally activated decay on microsecond timescales, the acceleration region is characterized by rapidly varying electric fields and nanosecond residence times such that dissociation becomes strongly field-enhanced. In this work, microcanonical ensemble molecular dynamics (MD) trajectories are used to infer dissociation kinetics of EMI-BF4 ion clusters under controlled internal energy and applied electric field. Positive and negative dimers, trimers, and tetramers are simulated at temperatures between 600 to 1000 K and uniform fields of 10^6 to 10^9 V/m. Dissociation lifetimes are extracted using a connectivity-based fragmentation criterion, and product channels are classified to obtain pathway-resolved branching probabilities. Across all solvation numbers, lifetimes are field independent in the weak field limit, but collapse by orders of magnitude above 10^8 V/m. Cluster polarity dependence is most prominent in the temperature-controlled regime and diminish as electrostatic work dominates. The MD simulations further reveal that field-driven changes in dissociation topology, including a transition in trimer breakup from neutral pair evaporation to charged core ion ejection, and three competing tetramer pathways involving single neutral loss, double neutral emission, and core ion ejection. The MD dataset is reduced to compact parameterizations of {\tau_n}(E,T), yielding transferable kinetic inputs for multiscale plume transport models.

Gravitational evidence firmly establishes the existence of dark matter (DM), yet its non-gravitational interactions and fundamental nature remain unresolved. In this work, we demonstrate that a chirality boost-spin gauge field, which emerges naturally in the General Standard Model formulated within gravitational quantum field theory, offers a vector DM candidate. We construct the corresponding low-energy effective field theory for this DM with axial-vector couplings to Standard Model fermions and systematically investigate its signatures in direct detection, indirect detection, and collider experiments. Comparing our predictions against current experimental data places stringent constraints on the DM parameter space, with the viable regions within the further reach of future searches.

Quantitative analysis of experimental powder X-ray diffraction data remains challenging when evaluating complex multiphase materials and noisy measurements. We introduce a hybrid quantum neural network framework designed to extract quantitative parameters, such as phase weight fractions and scale factors, directly from one-dimensional powder diffraction patterns without iterative refinement. The model combines noise-aware classical simulator pre-training with fast downstream fine-tuning on quantum processing unit features, ensuring stability against hardware decoherence. We demonstrate the practical utility of this approach by deploying the trained network onto an IBM quantum computer to analyse experimental X-ray diffraction computed tomography datasets from a three-phase solid oxide fuel cell containing ca. 10,000 patterns and a four-phase lithium-ion battery containing ca. 20,000 patterns). The network successfully reconstructs quantitative spatial phase maps in strong agreement with classical Rietveld refinement, paving the way for using quantum computing hardware to analyse real-world materials characterisation data.