Papers

Tropical cyclone landfall angle strongly modulates storm-surge potential, but how it changes with climate remains unknown. From 1980 to 2024, the global fraction of near-perpendicular landfalls shows a weak, statistically insignificant increase, with no significant basin-scale trends. PepC-Global simulations driven by reanalyses reproduce observed landfall-angle and pre-landfall approach-angle distributions and broad geographic variations in the near-perpendicular fraction. When driven by output from 14 CMIP6 models, the simulations show a similarly weak and statistically insignificant increase during 1980-2024, suggesting a forced contribution not yet detectable above internal variability. Under high emissions, however, the global near-perpendicular fraction increases significantly through 2099, with the strongest and most spatially coherent changes along the eastern North Pacific, whereas no significant global trend emerges under intermediate emissions. The projected increase arises mainly from changes in near-perpendicular fractions at individual coastal locations rather than from a redistribution of landfalls. These results identify landfall angle as an overlooked dimension of changing tropical cyclone coastal risk, one whose evolution this century depends on the emissions pathway.

Science teachers frequently search for documentary excerpts not by describing what appears on screen, but by querying the abstract concepts they intend to teach. This use case exposes a limitation of existing language-based video moment retrieval methods, which typically assume that queries describe observable events, whereas instructional search requires retrieving concrete visual phenomena that instantiate an underlying scientific principle. We study this setting as concept-to-example video retrieval, an abstract-needle-in-a-haystack problem where compact curriculum concepts must be grounded in temporally sparse documentary evidence. To bridge this abstraction gap, we propose Concept-Driven Domain Adaptation (CDDA), a three-stage framework for adapting two-tower vision-language models to concept-level retrieval. CDDA treats concepts as intermediate semantic anchors: it first structures the textual embedding space with textbook and teacher-handbook example-concept pairs, then transfers this concept-aware geometry to documentary visuals under a frozen visual encoder, and finally jointly adapts both encoders with sparse visual concept supervision. From a geometric perspective, this staged alignment reduces text-concept and vision-concept angular gaps, thereby encouraging concept-level adaptation while preserving the pretrained model's concrete image description alignment. On a curated middle-school physics retrieval benchmark, CDDA achieves stronger pedagogically oriented concept retrieval than several competitive multimodal baselines, including Qwen3-VL-Embedding-2B, while maintaining concrete image-text matching after adaptation.

Tidal disruption events (TDEs) can launch sub-relativistic outflows that drive shocks into the surrounding circumnuclear material (CNM), providing a natural site for cosmic ray (CR) acceleration and high-energy neutrino production through hadronuclear ($pp$) interactions. We model this emission for optically bright and infrared (IR)-only TDEs, the latter motivated by a recently identified population of luminous IR transients with weak or absent optical counterparts, consistent with TDEs embedded in dusty, obscured nuclear environments. We semi-analytically compute the shock dynamics, CR acceleration and transport, and neutrino production, including the radiative cooling and compression in dense environments. While optical TDEs remain inefficient $pp$ neutrino sources with comparatively small neutrino yields, radiative compression in dense IR-only TDEs enhances the target density and allows the pp efficiency to approach the calorimetric regime, yielding up to $\sim 10\%$ of the observed diffuse neutrino flux at $100$ TeV, given the rate uncertainties. The long-lasting neutrino emission and low individual source yield motivate joint electromagnetically informed stacking searches. We forecast such searches for IceCube, IceCube-Gen2, KM3NeT, and HUNT, and conclude that a future $\sim 30\ {\rm km^3}$ neutrino observatory can reach $3\sigma$ sensitivity for nearby ($z \lesssim 0.4$) IR-only TDEs with multi-year search windows. Growing optical and IR TDE samples can therefore enable population-resolved searches that can test whether dense, obscured nuclear environments are efficient high-energy neutrino sources.

Band topology is usually assigned to a single crystal structure, yet at finite temperature a crystal is an ensemble of thermally disordered configurations with a fluctuating band gap. We ask whether the thermally averaged structure is a faithful proxy for this ensemble, taking Bi$_2$Se$_3$ as a representative topological insulator and classifying band ordering in each ab initio molecular dynamics snapshot by scaling the spin-orbit coupling. The averaged structure fails in two ways: it misses the Rashba-like spin splitting that instantaneous symmetry breaking produces in individual configurations, and it substantially underestimates the band-gap renormalization, whereas a small ensemble of harmonic Monte Carlo configurations reproduces both the mean and the spread of the gap. Using a band-inversion survival probability, we find that the band inversion of Bi$_2$Se$_3$ holds in every sampled configuration up to 600 K, above which individual configurations cross into normal band ordering well before the average gap closes. Topological robustness at finite temperature is thus a property of the distribution over configurations rather than of any single structure.

We study a real-valued max-plus particle system on a finite periodic lattice. Its three-dimensional fundamental diagram relates the mean flow to two densities: the conserved particle density and a second density defined by a local state function. We prove that the deviation from the diagram tends to zero for arbitrary initial data with all state values in [0,1]. The proof uses two nonnegative local quantities and their evolution relations. For rational initial data with all state values in $[0,1]$, the orbit is eventually periodic, and the fundamental-diagram relation holds exactly on the periodic orbit.

Atomic layer deposition (ALD) offers precise, conformal control of thin-film growth and is a workhorse for semiconductor manufacturing, but its use in superconducting quantum circuits is constrained by the need to simultaneously control crystallinity, stoichiometry and interfacial disorder at cryogenic temperatures. Here we develop hydrogen plasma assisted ALD to epitaxially grow superconducting titanium nitride (TiN) thin films, using a hydrogen plasma step to modify the surface chemistry to suppress precursor-derived impurities during each deposition cycle. Synchrotron X-ray scattering reveals semi-coherent epitaxy of TiN on c-plane sapphire, with discrete crystallographic domains and minimal long-range structural disorder. Complementary X-ray spectroscopy and neutron reflectometry show negligible oxygen-related disorder throughout the film and no substantial hydrogen incorporation. The resulting TiN films exhibit a superconducting transition at 2.2 K, a kinetic inductance of 15 pH/sq and a superconducting coherence length of 14.6 nm, comparable to the measured crystal coherence length. These results establish hydrogen plasma assisted ALD as a route to structurally ordered superconducting TiN thin film epitaxy and demonstrate that the nanoscale control of ALD can be extended to materials suitable for cryogenic quantum technologies.

This paper studies the focusing Calogero–Moser derivative nonlinear Schrödinger (CMdNLS) equation under the nonzero boundary condition \(|u(t,x)|\to 1\) as \(|x|\to+\infty\). In sharp contrast to the classical paradigm for the cubic nonlinear Schrödinger equation, namely that focusing nonlinearities support only bright solitons on a zero background, we show that the focusing CMdNLS equation admits a rich family of dark multi-soliton solutions on a nonzero background, whose existence is driven by a spectral gap \([-1,0]\) in the point spectrum of the Lax operator, together with the Hardy space structure. The main results are threefold. First, we establish an explicit formula for general solutions in the Hardy–Zhidkov space \(\mathcal Z^2_+\), via a semigroup method based on new commutator estimates between the Lax–Beurling shift semigroup and Toeplitz operators. This approach circumvents the non-commutativity of the unbounded generator with the time derivative and requires no weighted hypothesis on solutions or on the domain of validity of commutator formulas. Second, the algebraic definition of dark multi-soliton potentials is shown to be equivalent to two spectral conditions on the Lax operator. Together with the conservation of a generating functional, this yields the invariance of the dark \(N\)-soliton set \(\mathcal U_N\) under the CMdNLS flow. Third, the action-angle variables on \(\mathcal U_N\) are constructed, which establishes the complete integrability of the focusing CMdNLS equation on \(\mathcal U_N\). Combining these results, we obtain an inverse spectral formula, from which it follows that the poles of dark multi-soliton solutions satisfy a complexified Calogero–Moser system. Long time asymptotics and uniform Sobolev estimates are also derived.

Discretizing electronic degrees of freedom into atomic orbitals is the default choice to accurately model electrons in molecules in a compact and flexible way. We consider the task of simulating such electronic structure models on a quantum computer for $N$ orbitals, keeping the number of orbitals per atom constant. Atomic orbitals give rise to Hamiltonians with potentially up to $\mathcal{O}(N^4)$ terms and little structure, which made it challenging to match the complexity of methods based on plane waves and real-space grids. In this work, we show that a Trotter step for atomic orbitals can be implemented in depth $\mathcal{O}(\mathrm{polylog}(N))$ without ancillas by combining a rigorous treatment of orbital localization, a hierarchical Hamiltonian decomposition based on the Fast Multipole Method, and shallow quantum Fourier arithmetic circuits. By bounding the Trotter error, we obtain sublinear simulation depth, and our total gate count $N^{5/3 + o(1)}$ matches the best scaling known for any basis.

One of the most important initial steps in quantum computing is high-fidelity quantum state preparation, because errors in it can affect the final computational result. Therefore, one of the major challenges is to design a quantum circuit that can achieve the desired target state accurately with a smaller number of gates. Traditionally, circuits are designed using predefined rules and mathematical decompositions, but this makes it difficult to design circuits for different and complex target states. This paper presents TATVA, a reinforcement learning (RL) system for synthesizing quantum circuits. TATVA views circuit synthesis as a sequential decision problem in which the agents select one gate at a time and calculate the fidelity after each applied gate, with the gate producing the highest fidelity selected for the circuit. The system introduces a parallel architecture that uses two RL agents, Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), along with Qiskit's statevector simulator. The system can synthesize up to 5 qubits while achieving a fidelity of 0.999999 and producing compact circuits. After achieving the desired fidelity, it performs a post-hoc optimization step that reduces the circuit depth while preserving the achieved fidelity. The circuit is finalized using fidelity, success rate, gate count, and circuit depth. Target states that are not used during training are used to test whether the system has learned effectively and can generalize beyond the training states. This approach aims to enable automatic quantum circuit synthesis with high fidelity using reinforcement learning.

We present CAFES, a correlation-aware agentic framework for electronic-structure simulations of strongly correlated systems. CAFES addresses two challenges: the fragmented software landscape for many-body calculations and the difficulty of selecting appropriate methods across diverse correlation regimes. It combines correlation diagnostics, adaptive method selection, and large language model (LLM) assistance for molecular, crystalline, and model-Hamiltonian systems. A study-task architecture separates study-level planning from task-level execution, while a curated scientific knowledge layer provides reusable guidance for method selection, workflow design, and result interpretation. We demonstrate CAFES through three research-level studies: calculating the low-lying electronic states of lutein using DMRG-CASSCF, probing phase competition in the extended honeycomb Hubbard model using DMET, and generating a quantum-chemical dataset with CCSD labels. These calculations demonstrate the potential of agentic workflows for strongly correlated electronic-structure problems, a regime that has received limited attention in existing agentic computational frameworks.

We investigated the nucleation, growth, and solid-to-solid phase transition processes of spherical six-fold symmetric crystals (C6) and ten-fold symmetric quasicrystals (DQC) using the Lifshitz-Petrich model. By combining the spherical-harmonic pseudospectral method with a nullspace-preserving saddle-point search technique, we resolved the critical nucleus structures and phase transition pathways. Although both transitions from the homogeneous state to the ordered state involve the formation of localized critical nuclei, their subsequent growth processes exhibit distinct modes of accommodating geometric mismatch: C6 forms branched domain structures and persistent line defects associated with orientation and phase mismatches, whereas DQC relaxes collision-induced defects through local coordination rearrangements. The resolved DQC-to-C6 pathway proceeds through a series of metastable intermediates that progressively connect these distinct modes of mismatch accommodation. Its multi-step character reflects the coupled adjustment of local coordination and compatibility between the emerging C6 domains. These results reveal the intrinsic link between the multi-step nature of the phase transition and the distinct structural requirements for accommodating geometric mismatch.

Magnetic field-line transport in magnetically confined plasmas is commonly modeled as diffusion through stochastic magnetic layers generated by resonant perturbations. However, experimentally relevant magnetic configurations frequently exhibit transport rates that are substantially slower than predicted by fully stochastic models, indicating the presence of unresolved dynamical barriers. In this work, we investigate the role of nested resonant island chains and their associated invariant manifolds in regulating transport within the edge magnetic topology of DIII-D tokamak discharge #171491 subjected to strong n=3 resonant magnetic perturbations. Using field-line tracing, Poincare analysis, and manifold calculations, we identify a hierarchy of homoclinic and heteroclinic tangles associated with both period-one and period-ten hyperbolic points. A systematic construction based on primary intersection points is developed to extract partial transport barriers from the full manifold geometry and divide the chaotic region into dynamically distinct domains. Monte Carlo simulations reveal that field-line escape exhibits a bi-exponential character when only the outermost period-one boundary is considered, reflecting the presence of a long-lived "sticky" region associated with the period-ten island chain. When the inner period-ten boundary is explicitly incorporated into the Monte Carlo, the escape process becomes approximately single exponential and is accurately described by turnstile lobe transport. The measured escape rate agrees with predictions obtained from the turnstile lobe area and the symplectic structure of the field-line map. These results demonstrate that nested tangles provide a quantitative transport skeleton governing magnetic-field-line escape and establish a framework for analyzing transport barriers, cantori, and lobe dynamics in realistic fusion-plasma magnetic configurations.

Rare-earth (RE) atoms, such as erbium (Er), have inner-shell optical transitions in the near-infrared, some of which are in the telecom bands. These transitions are largely insensitive to the host environment due to effective shielding by the filled outer shells. Cryogenic rare-gas (RG) solids, such as solid neon (Ne) and argon (Ar), combine high chemical purity with a nearly nuclear-spin-free environment, making them attractive solid hosts for preserving optical and spin coherence. In this work, we isolate neutral Er atoms in solid Ne and Ar at 6 K and characterize their emission by steady-state and time-resolved photoluminescence (PL) spectroscopy. In both hosts, we observe several near-infrared transitions between 1000–1500 nm, including inner-shell emission in the telecom O and E bands. The $4f \rightarrow 5d$ transition near 1299 nm forms a multiplet of narrow components, consistent with crystal-field splitting, and decays on a microsecond timescale. The $4f \rightarrow 4f$ transition near 1437 nm shows a matrix shift below 1 cm$^{-1}$ in both hosts. In solid Ne, its inhomogeneous linewidth is bounded by the spectrometer resolution to < 22 GHz, and its PL lifetime reaches up to 78.9 ms, close to the estimated radiative limit. Thermal annealing extends the 1437 nm lifetime in Ne and suppresses the phonon sidebands relative to the zero-phonon lines in Ar. These results identify RG solids as promising hosts for long-lived telecom-band Er emission and provide a foundation for future spin-photon interfaces and hybrid quantum architectures.

Binary pulsars provide valuable laboratories for testing theories of gravity, but orbital Doppler shifts complicate their detection. Fourier-domain acceleration and jerk searches address this challenge via matched filtering, but at substantial computational cost. We present LeoNet, a convolutional neural network that uses ten learnable filters to extract features of signals affected by Doppler shifts. The resulting ten-channel feature map provides a compact, lower-dimensional alternative to an explicitly sampled acceleration-jerk response grid and is analysed by a convolutional classifier to identify candidate signals. For simulated observations lasting 500 s, LeoNet achieves a mean relative reduction in false negative rate of 55.5% across five sampling intervals compared with the evaluated PRESTO acceleration-search configuration. TensorRT-optimised LeoNet processes each 500 s observation in 3.44-4.37 ms in FP32 on an NVIDIA H100 PCIe GPU across eight sampling intervals, including preprocessing, inference, and postprocessing. At a sampling interval of 128 microseconds, its mean processing time is 3.54 ms, compared with 1.767 s for PRESTO FDAS on an AMD EPYC 9825 CPU with search-frequency limits of 96-1000 Hz, corresponding to an approximately 499-fold speedup in the measured processing time. These results suggest that LeoNet has the potential to improve detection performance, while its millisecond-scale processing time supports its use as a candidate-identification stage in real-time binary pulsar search pipelines.

Understanding how macroscopic wealth distributions emerge from microscopic transaction rules among agents is a central problem in econophysics. While many traditional models restrict agents' wealth to non-negative values, debt is an essential feature of realistic economic systems. In this paper, we introduce a stochastic transaction mechanism into the agent-based model with a central-bank-mediated collective debt limit, as studied in [14]. In our model, each agent probabilistically determines whether to transfer a coin, depending on their current asset or debt level. From a probabilistic perspective, the resulting system can be formulated as a zero-range process with $\mathbb{Z}$-valued site occupation numbers, where negative values represent debt. We prove that, under an appropriate scaling, the wealth distribution in the stationary state converges to either a two-sided Gamma distribution or a Gamma distribution, depending on the asymptotic behavior of the function governing coin transfers. Our results rigorously identify these previously unconsidered limiting distributions and provide a mathematical framework for understanding how stochastic monetary exchanges give rise to macroscopic wealth distributions.

We propose a deviatoric-stress constitutive equation derived from orientational kinetics, while implicitly retaining contributions from conformational stretch. Starting from the dumbbell model, we show that conformational stretch affects orientational dynamics through an effective stress scale and rotational diffusivity, which determine an effective relaxation time. The orientation-based equation is derived by assuming an approximate relation between orientational anisotropy and the stretch state. Within the proposed framework, constant effective coefficients yield steady-oscillatory shear correspondences closely related to the empirical Cox-Merz and Gleissle-Osaki relations. For model-specific effective coefficients, the upper-convected Maxwell model is characterized by a constant relaxation time and an increasing modulus under steady shear, whereas finite extensibility with Peterlin preaveraging shortens the relaxation time and suppresses the modulus increase. With coefficients determined from steady-shear states, the proposed equation successfully reproduces stress responses under transient shear, large-amplitude oscillatory shear, and uniaxial extension. These results show that the effects of conformational stretch on orientational kinetics can be retained implicitly, providing a constitutive description based on a deviatoric-stress state reconstructable from standard rheometry and a physical interpretation of how stretch enters polymer viscoelasticity.

The high-energy and astroparticle physics literature has grown into an enormous body of knowledge. Gaining a comprehensive understanding of this literature is an important step in research, but is a challenging task. Before making meaningful advances, it is important to establish what has already been done and identify open questions. However, this process is becoming increasingly difficult given the sheer volume of publications. Conducting a focused literature survey on a narrow topic is particularly challenging. We develop an open-source Retrieval Augmented Generation (RAG) pipeline to produce concise, targeted summary reports with citations, grounded in the database of arXiv papers, enabling researchers, editors, and referees to rapidly orient themselves within any corner of the field. Specifically, we embed approximately 230K hep-ph and astro-ph.HE papers to enable a hybrid retrieval that captures both keyword matches and semantic similarity. The fused candidate set is then refined using a cross-encoder reranker. Following retrieval, the papers are passed to a Large Language Model (LLM), for which we use Google DeepMind's open-source Gemma-4-E4B model. The LLM first acts as a judge to evaluate the relevance and then synthesizes a coherent and grounded report with references. This modern AI-driven approach allows us to go beyond the capabilities of classical keyword search on arXiv or Inspire-HEP.

Codon optimization is a challenging combinatorial optimization problem with important applications in synthetic biology, protein expression, and biotechnology. While quantum annealing has previously been explored for this problem, the application of gate-based quantum algorithms remains largely unexplored. In this work, we present a gate-based quantum computing framework for codon optimization by formulating the optimization objective as an Ising Hamiltonian and expressing it in terms of Pauli operators suitable for gate-based quantum processors. The proposed framework is investigated using the Variational Quantum Eigensolver (VQE), Sampling Variational Quantum Eigensolver (SVQE), and the Quantum Approximate Optimization Algorithm (QAOA), with benchmarking against exact diagonalization, classical optimization, and quantum annealing. Using a three-amino-acid benchmark, the proposed implementations successfully reproduce the exact ground-state solutions while identifying optimized ansatz selections, optimization strategies, initialization schemes, and hardware execution settings that provide reproducible performance on current noisy quantum hardware. Hardware demonstrations are performed on IBM Quantum processors to evaluate the practical implementation of the proposed workflow. As a work in progress, this study establishes a reproducible computational framework for gate-based quantum codon optimization and provides the foundation for future investigations of larger protein sequences as quantum hardware and algorithms continue to advance.

We study the spectral properties of the harmonic modification of the quantum mechanical Kronig-Penney model motivated by the theory of the dressing chain. In particular, we show that in the special cases the corresponding ground states can be given explicitly and all spectral gaps have the same length with the ends expressed as zeros of the corresponding Weber-Hermite functions.

Chemical intercalation provides a powerful route for tuning the electronic and magnetic properties of transition metal dichalcogenides (TMDs), enabling the emergence of magnetic phases and phenomena that are otherwise inaccessible in their pristine forms. Here, we investigate the structural, electronic, and magnetic properties of magnetically intercalated Cr$_{1+x}$Te$_2$ through a combination of scanning tunneling microscopy, angle-resolved photoemission spectroscopy, magnetization, electrical transport, and Lorentz transmission electron microscopy measurements. We uncover a rich magnetic phase diagram comprising paramagnetic, in-plane spin-fluctuation, out-of-plane ferromagnetic, and field-induced Néel-type skyrmion phases. In particular, Lorentz transmission electron microscopy directly reveals the formation of Néel-type skyrmion textures slightly below the Curie temperature, with their configurations evolving strongly with temperature and magnetic field. The skyrmions exhibit pronounced thermal hysteresis and coexist with stripe domains, indicative of a complex magnetic energy landscape arising from the competition among disorder-induced local Dzyaloshinskii-Moriya interactions, perpendicular magnetic anisotropy, and dipolar interactions. Our results demonstrate that chemical intercalation can profoundly reshape the magnetic interactions and phase behavior of TMDs, establishing Cr$_{1+x}$Te$_2$ as a versatile platform for exploring tunable magnetism, skyrmion physics, and unconventional thermodynamic phenomena in low-dimensional quantum materials.

Machine-learned interatomic potentials are commonly fitted by weighted-sum scalarization, which combines energy and force errors in a single loss. A nominal weight, however, identifies a potential only relative to the complete fitting protocol. We therefore treat energy–force balancing as a protocol-dependent problem of physical model selection. For fixed-basis atomic cluster expansion models of molten LiCl, liquid H$_2$O, and Si, the resulting scalarization paths contain dominated states at extreme weights. Their nondominated subsets depend on the solver, and the out-of-distribution Si path is nonmonotone. We replace direct weight selection by minimizing the regularized energy objective subject to an upper bound on the normalized force loss. Projected dual ascent adjusts the Lagrange multiplier from the force-constraint residual. A frozen-multiplier limited-memory quasi-Newton refinement then returns the final model. Across the three systems, the constrained procedure reaches the solver-matched low-error region and gives measured speedups of $6.7$–$8.9$ over completed scalarization scans under the stated timing convention. Physical-property calculations show that first-shell geometry is comparatively insensitive to the selected balance, whereas transport and solid-state observables vary more strongly. These results identify the fitted model–protocol pair as the relevant object of energy–force model selection and support force-constrained fitting as an explicit selection rule.

We study endpoint mapping properties of low-energy wave operators for Schrödinger operators $H=-\Delta+V$ on $\mathbb R^n$, $n\ge3$. In dimension four, we prove that a zero-energy resonance prevents $L^1$ boundedness, whether or not zero is also an eigenvalue, under $|V(x)|\lesssim\langle x\rangle^{-\beta}$ with $\beta>10$. For a zero-energy eigenvalue without a resonance, we obtain a complete low-energy $L^p$ classification in every dimension $n\ge3$ under $\beta>n+4$. In particular, $L^\infty$ boundedness is equivalent to the vanishing of the zeroth, first, and harmonic second moments of $V\psi$ for every zero-energy eigenfunction $\psi$. The proof identifies the finite-rank obstruction and shows that the remaining eigenvalue correction cannot cancel its critical asymptotic profiles. The same conclusions hold for the full wave operators when the corresponding high-energy bounds are available.

A 35 input/single output quantum phase neural network (QPNN) is proposed to recognize the English letters' A 'and' B 'composed 7*5 of pixels. Each node of the proposed QPNN has the input with 0 or 1. Through normalization and quantum phase encoding, each set of 35 with 0 or 1 digital inputs is encoded into a quantum superposition state described by 35 quantum phases. Then, a quantum rotation gate is used to introduce adjustable phase, and a controlled NOT gate is used to entangle the relative phase between the two inputs. The relationship between the input and output of the network is derived. This paper also derived the phase weight learning training algorithm with adaptive learning rate. The solutions with the recognition probability of 1 is designed, and the analytical expressions for all adjustable phase solutions with zero errors are not unique. The experimental performance is verified on the Qiskit platform. This paper uses phase drive to convert the probability calculation into an analytical polynomial function with adjustable rotation angles in the network, avoiding the bottleneck of generating all 2^35 complex exponential amplitudes and providing an effective way to solve the "exponential wall" problem, which provides a new implementation solution for the practical application of multi-input recognition problems.

We construct a two-parameter family of single-error-correcting seven-ququart codes with transversal $\tilde{A_7}$ symmetry, realizing the finite component of a two-qubit super-golden gate set. These $((7,4,3))_4$ codes encode two logical qubits and support non-Clifford operations by applying the same gate to each physical ququart. Our method constructs invariant parent states rather than searching directly for a codespace. For $n$ systems of local dimension $q$, suppose the local symmetry is a unitary $t$-group and the permutation symmetry is $t$-transitive. Maximal mixing of all marginals on at most $t$ sites is then equivalent to linear equations in Schur–Weyl sector weights. Every feasible solution yields parents of pure distance at least $t+1$; puncturing one site produces a $q$-dimensional code of distance at least $t$ with the prescribed local symmetry acting transversally. Steane and the nonstabilizer Fake Steane code reveal why the construction leaves continuous freedom. They share the same prescribed transversal Clifford and permutation symmetries, and their eight-qubit purifications lie on a circle: error correction fixes two invariant-sector norms but leaves the relative phase free. Keeping the same permutation group and replacing Clifford symmetry by $\tilde{A_7}$ $\leq SU(4)$ gives three fixed sector norms and two free relative phases, producing the new ququart family.

We investigate vortex nucleation and dynamics in a dipolar Bose-Einstein condensate stirred by a rotating Gaussian obstacle. Two stirring protocols are compared: continuous stirring with constant amplitude (Case 1), and obstacle removal at $t = 15$ ms with linear amplitude ramp-down (Case 2). In both cases, a smooth angular velocity ramp ($t_{\text{acc}} = 3$ ms) suppresses spurious phonon excitations. The first vortex pairs exit the obstacle at $t \sim 3$ ms, and by $t \sim 8$ - $10$ ms the individual vortices and antivortices are fully resolved in the phase maps. Case 1 yields a vortex population that grows up to $t \sim 40$ ms and then roughly saturates, while Case 2 yields a population that peaks around $t \sim 15$ - $25$ ms and then decreases. Remarkably, both protocols lead to local triangular-like ordering in selected regions of the condensate by $t \sim 50$ ms, exhibiting clear dipolar signatures: elliptical distortion and elongated vortex cores along the polarization axis. The mixed vortex-antivortex population remains stable up to $100$ ms with no observable annihilation, indicating that dipolar interactions strongly suppress vortex decay. Kinetic energy decomposition confirms that incompressible (vortex) energy dominates at late times. These results establish a controlled platform for vortex studies in dipolar superfluids and provide benchmarks for future experiments on quantum turbulence with long-range anisotropic interactions.

The equalities IP = PSPACE and QIP = PSPACE, the latter achievable with three messages, raise a basic question: how much of an interactive proof's power comes from the underlying physical theory? We study interactive proofs in general probabilistic theories, which include classical and quantum theory. The answer depends on what the prover and verifier exchange and how the theory specifies efficient operations. When they exchange only classical messages, protocols in every theory satisfying our standard assumptions decide exactly PSPACE. For protocols with a quantum verifier and quantum messages, allowing a prover to use any theory containing quantum theory does not increase the maximum acceptance probability. Thus, the three-message PSPACE result remains valid against such provers. When messages may be arbitrary systems, the interactive-proof class can strictly exceed PSPACE.

Machine learning models are increasingly used in feedback control loops for nuclear fusion, where inference speed and predictable timing are critical. We summarize lessons from models deployed for control on the DIII-D tokamak and develop a benchmark to compare inference backends across ten neural networks and model components from fusion control and diagnostic pipelines. For models greater than five million parameters, the CPU backends take tens to thousands of milliseconds, while GPU inference is substantially faster, suggesting an upper limit on CPU-oriented development for control. These results show why the deployment backend must be selected together with the model and its control-cycle budget.

The Wigner function, in general, takes on negative values, and the amount of negativity in the Wigner function gives an operationally meaningful measure of the complexity of simulating the quantum state on a classical computer. In this paper, we study the growth of Wigner negativity of the Hartle-Hawking state of Jackiw-Teitelboim gravity under time evolution. We work in the gravitational length basis, at genus zero, and at high temperature, $\beta \ll 1$. Our main analytic result is simple: to leading order in $\beta$ the state is a Gaussian wavepacket of minimum uncertainty, sitting at rest at the turning point of the Liouville wall. Its Wigner function is therefore positive, and the negativity is $1+\delta(\beta,t)$, where the correction $\delta$ is smaller than any power of $\beta$. We show that $\delta$ is an even function of time, so there is no linear growth at $t=0$, and that once the packet has reflected off the wall its leading-order evolution is a Clifford shear, which cannot change the negativity. We also numerically study the time evolution of the Wigner function and its negativity. We observe that $\delta$ stays below $10^{-11}$ at $t=0$ and through the reflection, then rises slowly over a few tens of $\beta/\pi$, and then approaches a late-time value consistent with $\delta_\infty(\beta) \simeq 0.08\, e^{-4.4/\beta}$ for $0.25 \le \beta \le 1$: the lower the temperature, the larger the late-time negativity. The exact survival amplitude $Z(\beta+it)/Z(\beta)$ fixes the spread complexity to order $t^4$, and it also fixes the seed-normalised Wigner diagnostic of our earlier work (arXiv:2607.04065, arXiv:2607.17346). We take this as evidence that the length basis is ideally suited for a dual, semi-classical description of the dynamics at genus zero. Beyond genus zero the length basis is overcomplete, and we make no statement about finite $e^{S_0}$.

Thin filaments cross in microscopy, and dispersed spectra cross on X-ray detectors. At a junction, where these structures cross or branch, how many straight arms meet, and which way do they go? Unknown blur obscures the answer. At a balanced crossing, distinct Gaussian blurs can even produce the same observation law. We show that the arms can still be recovered. For separated rays of nonvanishing weight and an unknown centered Gaussian-mixture blur, a distance combining arm count, directions, relative weights and supporting lines is bounded by a constant times the Euclidean difference in cell probabilities on a sufficiently fine fixed grid. The bound is uniform over a bounded model class, including unidentifiable blurs. From $n$ positions or photon counts, the minimax error is of order $n^{-1/2}$. This rate is proved for a global estimator. In practice we fit rays and blur by constrained local likelihood, reducing the BIC count when two detectors agree on fewer arms in the microscopy studies. Across three studies with protocols fixed before evaluation, on 360 crossings and branches assembled from measured single-molecule localizations, the fit gets the arm count and every direction within $10^\circ$ 24 to 37 percentage points more often than a tuned fixed-scale Hessian ridge detector, averaged over 25 to 400 localizations. On acute angles with correct counts, mean absolute error is $3.5^\circ$ to $4.3^\circ$, versus $10.4^\circ$ to $15.5^\circ$. On Chandra spectra, 38 of 90 fits at 800 photons find all four arms within $10^\circ$; among these, median error is $0.023^\circ$.

Real-world physics often unfolds over a variety of characteristic lengths and time scales. These non-uniformities are among the main reasons numerical simulations remain expensive when used predictively on practical systems: the same computational domain can contain smooth transport, vortical structures, thin interfaces, reaction fronts, and rapidly relaxing regions. Neural operators are attractive surrogates for such problems. However, a single architecture is still expected to represent all these local behaviours with a single set of weights and a single inductive bias. In this work, we introduce the regime-aware coupled neural orchestrator (RCNO), a framework that learns to distribute and connect a library of local operator specialists in space and time. For every patch, an orchestrator builds a causal query from the current fields, neighbouring halo values, local regime features, patch identity, activity level, and case parameters. The nine expert embeddings exchange two rounds of query-conditioned message passing before an edge-biased cross-attention stage produces a dispatch distribution that can be evaluated sparsely. Local predictions are assembled into a global field and projected onto system-specific physical constraints. A seam-aware extension additionally learns residual corrections across internal patch interfaces. We apply the same orchestration architecture to two qualitatively different systems: reacting flow and Cahn-Hilliard-Navier-Stokes (CHNS) dynamics. The results show that useful operator specialisation exists across local regimes, that the advantage of conditional composition increases as the spatial decomposition becomes finer, and, on reacting flow, that explicitly treating patch interfaces improves continuity, PDE consistency, and rollout accuracy.