Papers

Fracture in large-scale structures — from failing concrete dams to calving glaciers — is dominated by self-weight. Yet, owing to their immense scale, directly studying when and where cracks nucleate and propagate in these systems under gravity is unfeasible. To date, experimental investigations have largely relied on mimicking self-weight in scaled-down specimens via centrifugal forces, necessitating specialized centrifuge facilities. In this Letter, we introduce a simple benchtop test that bypasses the need for this infrastructure. The method utilizes a cantilever beam containing a downward-pointing V-notch, loaded solely by its self-weight. Through appropriate selection of the notch geometry and placement and the beam dimensions, the setup forces a crack to nucleate at the notch tip and propagate through the specimen in a measurable manner. We demonstrate the utility of this self-weight fracture test on a 3D-printable mortar and analyze the results using the fracture theory recently introduced in (\citeauthor{LPKFG2026}, 2026). Inter alia, the findings provide a first validation of the theory for fracture driven by body forces.

Logical error rate is a standard benchmark for quantum error correction, providing an aggregate measure of the overall effect of physical noise. In actual QEC architectures, errors at different circuit locations can have markedly different effects on logical failure even when their probabilities are equal. Here we introduce an error attribution scheme to quantitatively characterize the influence of individual circuit components on the logical error rate and use this refined information to guide targeted intervention and design for improved QEC performance. Our attribution estimator computes all component sensitivities jointly from the same Monte Carlo samples used to estimate the logical error rate. The resulting sensitivity maps identify hotspots where targeted noise reduction can most effectively improve logical performance. We test the scheme in circuit simulations of rotated surface code memories and high-rate lifted product codes: halving the noise strength on about 5%-7% of physical components selected by sensitivity reduces logical error rates by approximately 15%-25%, roughly twice the mean reduction from the same intervention on an equal number of randomly selected components. By connecting performance benchmarking to targeted optimization, our scheme provides a practical strategy for improving quantum error correction with limited resources.

We investigate anisotropic domain wall and cosmological solutions in $D$-dimensional gravitational theories of arbitrary spacetime signature coupled to scalar fields with a scalar potential. We identify a class of scalar potentials for which the second-order equations of motion admit a first-order formulation. The associated bosonic flows allow non-trivial Ricci-flat worldvolumes, while their Killing-spinor realization further requires the worldvolume to admit a non-vanishing parallel spinor. Five-dimensional ${\cal N}=2$ gauged supergravity provides a concrete realization of this construction, with the worldvolume geometry determined by its signature and giving rise to pp-wave, hyper-Kähler, and hypersymplectic geometries. We further construct a general class of anisotropic solutions depending on a single variable, with the anisotropy encoded in a constant traceless matrix. A particular branch arises when a subset of scalar fields contribute through their kinetic terms but do not enter the scalar potential; these fields follow geodesics on the scalar manifold. As an explicit application, we recover the Kasner-dilaton-AdS solutions and show that, in the appropriate limit, they satisfy the first-order flow equations.

Polynomial transformations of data encoded in quantum operations are a building block of quantum algorithms, and their query complexity measures how often those operations are used. For multiple variables, existing methods either realize restricted polynomial families or implement monomials separately at a query cost exponential in the number of variables. We present a quantum circuit that implements every multivariate trigonometric polynomial up to an explicit normalization. The circuit processes one variable coherently over the frequency components of the remaining variables, allowing all components to share the same signal queries. Each variable is queried exactly as many times as its degree in the polynomial, and we prove that this query complexity is optimal. We further extend the construction to commuting unitaries. The resulting circuit applies the multivariate polynomial simultaneously across their shared eigenbasis, using the minimum numbers of forward and inverse queries to each unitary. We also study trainable rotations with fixed preparation and a local readout of the last input. At query depth at least two, independent uniform initialization gives every active readout gradient a variance lower bound set by its squared branch weight and the inverse square root of the depth, uniformly over data states and dimensions. For supervised squared loss, retaining sample correlations gives an initialization gradient-variance bound in terms of targets and input spectra. This also bounds the expected loss decrease after the first exact gradient step with a specified step size. These results provide a scalable and practical solution for developing multivariate quantum algorithms and quantum learning models.

Integrated photonic technologies require active materials capable of providing large optical modulation within compact footprints and low losses. The large optical nonlinearities of exciton-polaritons offer a promising route to meet these requirements, yet their integration into photonic circuits remains challenging because strong light-matter interactions must be combined with low-loss transport and spatially localized control. Here, we demonstrate active phase and amplitude modulation in halide perovskite polaritonic waveguides integrated with electrical microheaters. Local electrothermal actuation drives a structural phase transition in the perovskite, modifying the complex refractive index dispersion near the excitonic resonance. By tuning the excitonic fraction of the propagating mode, the waveguides can therefore operate either as a phase shifter with a tuning efficiency of 0.2 mW mm, approximately one order of magnitude lower than silicon modulators with similar geometries, or as an intensity switch , providing 1 dB/um modulation depth at 20 mW. Our work establishes phase-change excitonic reconfiguration as a route towards compact polariton circuits for nonlinear photonic applications.

Continuous-variable quantum key distribution (CV-QKD) networks require routing strategies that account simultaneously for secret key generation, relay trust and finite network resources. We introduce Quantum Utility Routing (QUR), a weighted routing framework for networks combining trusted point-to-point links with untrusted CV Bell measurement relays. Transition level composable finite size secret key rates (SKRs) are incorporated directly into the route search, allowing QUR to balance bottleneck SKR against route geometry and resource use. We consider graph optimized QUR (QUR$_{\mathrm{go}}$) and a fixed weight implementation (QUR$_{\mathrm{fw}}$) that uses machine learning to avoid repeated graph specific weight optimization, and benchmark both against A* and Max–Min routing. At the default network configuration, QUR$_{\mathrm{go}}$ and QUR$_{\mathrm{fw}}$ improve the mean path SKR relative to A* while requiring substantially fewer network resources than Max–Min. The framework also maintains secure connectivity over a broad range of trusted node availability by exploiting untrusted infrastructure as measurement relays. These results demonstrate that routing in CV-QKD networks can be treated as a configurable trade off between cryptographic performance and network resource consumption.

Quantum annealers can sample degenerate optima unevenly, so the composition of finite-read output matters when multiple solutions are sought. We investigate whether problem structure can steer this output through node-dependent anneal offsets for Minimum Vertex Cover (MVC). In MVC, vertex degree equals the number of incident edge-cover constraints and therefore measures local constraint participation. We use this relation to define degree-conditioned offset-scheduled quantum annealing (DC-OSQA), a static rule assigning more negative offsets to vertices with larger max-normalized degree. Experiments used a D-Wave Advantage2 system1 quantum processing unit (QPU). For each graph, DC-OSQA was compared with a zero-offset baseline using the same MVC encoding, fixed physical embedding, majority-vote chain decoding, and 1000-read budget. A sampled bitstring was counted as a certified candidate only if it represented a feasible vertex cover and its cardinality matched the minimum independently certified by integer linear programming. Across 120 paired comparisons on sparse Barabasi-Albert graphs with two attachments per added vertex and graph sizes N = 70-150, DC-OSQA increased the mean number of distinct certified candidates from 171.7 to 455.8 and the mean number of complete-linkage Hamming-distance families from 22.5 to 80.0; paired gains were positive at every tested size. These shifts, together with lower empirical concentration, indicate a change in the composition of the observed certified support. At N = 150, the degree-aligned assignment exceeded the within-instance mean over 20 random reassignments of the same offset values in 19 of 20 graphs for certified-support size and all 20 graphs for Hamming families. Within the tested ensemble, these results show that an MVC-specific degree rule changes the certified optima observed within a fixed QPU read budget.

Marriott and Watrous showed that quantum Merlin–Arthur games admit generic error reduction without increasing witness size [Computational Complexity, 2005]. In this work, we show that this state-size-preserving amplification property does not hold for polynomial-time quantum computation with quantum advice. In particular, we present decision problems for which even a vanishing additive error reduction requires longer advice. More precisely, for every polynomially bounded advice length $m(n)\geq n^4$ and every error bound $\varepsilon(n)$ that stays below $1/2$ by at least an inverse polynomial, there is a positive function $\delta$ with $\delta(n)=O\bigl(\min\{(\log m/m)^{1/4},\ \sqrt{\log m/m}\,/(1/2-\varepsilon(n))\}\bigr)$ such that $\mathsf{BQP}_{\varepsilon}/\mathsf{q}m \subsetneq \mathsf{BQP}_{\varepsilon + \delta}/\mathsf{q}m$; for constant $\varepsilon$ the gap is $O(\sqrt{\log m/m})$. Here, $\mathsf{BQP}_\varepsilon/\mathsf{q}m$ is the class of languages recognizable with error at most $\varepsilon(n)$ by a polynomial-time quantum algorithm with an $m(n)$-qubit advice state that only depends on the input length $n$. We show this by proving a stronger separation $\mathsf{P}_{\varepsilon+\delta}/\mathsf{r} m \not\subset \mathsf{BQP}_{\varepsilon}/\mathsf{q} m$, where $\mathsf{P}_{\varepsilon}/\mathsf{r}m$ is the class of languages recognizable with error at most $\varepsilon(n)$ by a deterministic polynomial-time algorithm with an $m(n)$-bit advice string sampled from a distribution that depends only on $n$.

The Any Light Particle Search II employs a highly sensitive heterodyne detection system to measure an ultra-weak electromagnetic field that is reconverted from axions inside a regeneration cavity (RC). The initial design of the heterodyne system utilized a local oscillator laser (LO) that was resonant with the RC to form an interference beatnote with the weak signal field exiting the cavity. A weakness of this design is that it is possible for the out-of-mode field component of the LO to be larger than the in-mode component, allowing its noise to dominate the optical component of the noise budget and reducing the SNR of the experiment. This problem can be addressed by introducing a second, non-resonant laser as the LO. Since the in-mode light of this laser does not couple to the cavity, significantly more of its power is reflected than when the LO is resonant. We present a model for the measurement noise that predicts these design changes increase the SNR by a factor of roughly 35. This model was then confirmed experimentally by injecting a weak signal to the RC and examining the difference in the sensitivity of the detection system in each of these configurations.

We investigate the performance characteristics of an active magneto gyrator that consists of an inertial charged active particle confined in a two-dimensional asymmetric parabolic potential coupled to two thermal baths maintained at two different temperatures and in the presence of a magnetic field applied perpendicular to the plane of motion. In contrast to the passive Brownian gyrator, where the power-efficiency trade-off imposes strict operational limits, the active magneto gyrator exhibits striking violations of this rule, allowing regimes in which both output power and efficiency increase simultaneously with load strength. Moreover, while the Thermodynamic Uncertainty Relation (TUR) predicts that highly efficient engines necessarily produce strongly fluctuating power, rendering the Brownian gyrator unusable as the efficiency approaches the Carnot bound, the active magneto gyrator circumvents this constraint. Specifically, it achieves high efficiency with minimal power fluctuations, thus defying the TUR. Remarkably, efficiency can even surpass the Carnot limit and approach 100%, without compromising stability. This condition cannot be achieved in the absence of either activity or a magnetic field. These results highlight the active magneto gyrator as a promising setup for designing stochastic heat engines that can transcend Carnot efficiency.

Renormalization group (RG) flows for systems subject to an external field can be derived either by fixing the field or by fixing the conjugate order parameter. We show how to implement these boundary conditions within the functional renormalization group approach. We derive and compare the corresponding flow equations for $\phi^4$ theory and show that some scaling variables and scaling exponents associated with the linearized flow of the relevant three-point vertex in the vicinity of the Ising fixed point depend on the boundary conditions. We also discuss the effect of elasticity on Ising criticality. In this case two of the fixed points (namely the renormalized Ising fixed point and the spherical fixed point) obtained for constant strain are shifted to physically inaccessible regimes of parameter space when we perform the RG at constant stress.

A well-known theorem states that the Clifford+T gate set is universal for quantum computing. The theorem combines approximation using a distance measure on unitary matrices with equality up to global phase. Previous proofs combine these two notions for specific distance measures, but do not identify the properties that govern how they work together. Which properties are sufficient to state and prove the theorem? We answer this question by defining projective distance measures using four axioms. We prove that the universality theorem holds for every distance measure satisfying these axioms. Thus, our formulation of the theorem is independent of any particular choice of distance measure. We show that the Hilbert-Schmidt distance is already a projective distance measure. We also develop a general construction that transforms a large class of distance measures into projective distance measures and apply it to obtain projective versions of the operator-norm distance, the Frobenius distance, and the trace distance. Finally, we formalize the proof of the universality theorem in Lean.

For the coupled three-dimensional double well $H=\tfrac12|p|^2+2\sum_i(q_i^2-1)^2-\sum_{i<j}q_i^2q_j^2$ we exhibit, at the first excited energy level (and also at the ground state), a linear functional on polynomials of degree $\le 6$ that satisfies every constraint of the standard level-3 eigenstate bootstrap, has a strictly positive definite moment matrix, and nevertheless assigns a negative value to a nonnegative polynomial. It is therefore an interior point of the level-3 relaxation that is realized by no quantum state. The construction reflects a fact that holds at every level: the eigenstate rows never reach the top-degree momentum moments. At level 3, where strictly feasible points exist, this means that the corresponding block of the moment matrix carries no information about which energies admit one. Every algebraic ingredient is an exact rational certificate, and the only numerical ingredients (spectral enclosures of an eigenfunction) are carried out in rigorous ball arithmetic.

Quantum computing has developed rapidly in recent years and has shown potential advantages over classical computing in various domains. In this paper, we study whether quantum computing can accelerate the convergence rate of discrete simulation optimization in the fixed-budget setting. We first formulate the quantum discrete simulation optimization problem by defining the quantum simulation oracle, the algorithmic performance measure, and the corresponding optimality guarantee. Based on this formulation, we establish a minimax lower bound on the expected optimality gap for any quantum algorithm. The lower bound analysis reduces the discrete simulation optimization problem to a sequential quantum phase-testing problem, and the resulting lower-bound argument may be of independent interest. By comparing the quantum lower bound with its classical counterpart, we show that the optimal convergence rate in the quantum setting exhibits a quadratic improvement in its dependence on the simulation budget, at the cost of a worse dependence on the number of decisions. We then propose the first fixed-budget quantum algorithm, called QUEST, for discrete simulation optimization. The algorithm combines a quantum Monte Carlo subroutine with a sequential elimination framework and uses a slow-measurement mechanism to help preserve quantum superposition during the optimization process. We prove that QUEST achieves the optimal convergence rate up to logarithmic factors. Numerical experiments further show that QUEST outperforms its classical counterpart, which provides empirical evidence for the quantum advantage in improving the convergence rate of discrete simulation optimization algorithms.

Describing fermionic systems with attractive interactions, such as superconductors and atomic nuclei, beyond the mean-field level remains a central challenge. In this work, we investigate the performance of Bogoliubov fully self-consistent $GW$ (Bog.~sc$GW$) and second-order Green's function (Bog.~scGF2) methods, implemented within a unified imaginary-time and imaginary-frequency framework. Using the attractive Hubbard model as a benchmark against full configuration interaction (FCI), we find that Bog. scGF2 provides a more accurate description than Bog.~sc$GW$. In particular, Bog.~sc$GW$ systematically overestimates the correlation energy, with errors increasing rapidly as the attractive interaction strength grows. These results highlight the importance of exchange contributions in strongly correlated fermionic systems, even when exchange effects are included only at low order in perturbation theory.

Climate oscillations are intrinsically out-of-equilibrium phenomena, yet the connection between their predictability and nonequilibrium thermodynamic properties remains unexplored. We develop a coarse-grained stochastic thermodynamic framework for Ornstein-Uhlenbeck (OU) processes and apply this framework to climate data via linear inverse models (LIMs), linking predictability, entropy production, dynamical activity, and path action. Climate oscillations are often studied in a 2d phase space of climate indices, which are even under time-reversal, motivating our focus on 2d OU dynamics with even variables. We find that coordinate-invariant properties of 2d OU processes can be reduced to a minimal three-parameter description: two diffusivities and a phase-space rotation. We show that these quantities fundamentally characterize distinct aspects of the dynamics. We find that higher irreversibility and dynamical activity do not necessarily imply reduced predictability. Rather, it is the path action which is most closely connected to a commonly used measure of predictability, the anomaly correlation coefficient (ACC). As an illustrative example, we apply this framework to the Madden-Julian Oscillation (MJO) over the twentieth century. We find that the MJO has become simultaneously more predictable, as seen previously, while at the same time becoming more irreversible and more active. Predictability is driven predominantly by the reduced mean diffusion. Entropy production reflects the combined evolution of all three parameters. These results suggest that stochastic thermodynamics provides a powerful lens for studying nonequilibrium climate phenomena.

Dynkin classified the maximal subalgebras of the simple finite-dimensional Lie algebras. The super version of his problem is solved, for the matrix Lie superalgebras, by Shchepochkina, and, for the simple vectorial ones with polynomial coefficients taken with their Weisfeiler gradings, in the companion paper by Leites and Shchepochkina — with three exceptions left open: the three simple finite-dimensional Lie superalgebras $\mathfrak{osp}_a(4|2)$, $\mathfrak{ag}(2)$ and $\mathfrak{ab}(3)$, which are vectorial in more than one way and are not covered by the general argument. Here we settle these three cases for graded subalgebras. For each of the three Lie superalgebras, we list all the $\mathbb{Z}$-gradings to which the method of that paper applies — two for $\mathfrak{osp}_a(4|2)$, five for $\mathfrak{ag}(2)$, eight for $\mathfrak{ab}(3)$ — and describe, for every one of them, all the maximal graded subalgebras, the semi-simple ones explicitly. A simple criterion upgrades most of the answers from “maximal graded\rq\rq\ to “maximal\rq\rq. Every answer is computed over $\mathbb{Q}$. Two by-products: the classical list of five cases into which the graded subalgebras of a vectorial Lie superalgebra split is incomplete for ambients of depth $>1$ — a sixth case has to be added, and it is non-empty precisely here; and the parameter $a$ of the copies of $\mathfrak{osp}_a(4|2)$ sitting inside $\mathfrak{ag}(2)$, resp. $\mathfrak{ab}(3)$, is computed intrinsically.

Quantum key distribution (QKD) is a fundamental functionality of quantum networks. Extending its range requires quantum repeaters, a technology still under development and not yet demonstrated in combination with quantum cryptographic protocols. Here, we address the elementary operation of a viable quantum repeater scheme by evaluating whether correlated photons distributed via entanglement swapping can support secret key distribution. We employ a state-of-the-art deterministic photon source based on a strain-tuned GaAs quantum dot embedded in a photonic cavity and implement a source-independent QKD protocol between two nodes using entanglement-swapped photons. We benchmark the system and present how high-resolution temporal post-selection can enable secure key exchange, with further rate improvements expected through realistic device and protocol optimizations. Due to its compatibility with a wide range of quantum memory platforms, our approach represents a significant step towards long-distance quantum-safe communication.

We consider the interaction-round-a-face version of the nineteen-vertex model and derive a new quantum spin-$1$ chain. In the isotropic limit, the associated spin chain can be seen as three coupled Babujian-Taktahjan spin chains with different boundary twists. We compute the ground state short-distance correlation functions of the isotropic model in the thermodynamic limit by exploiting the shared integrable structure between the models.

Major mergers are transformative events in galaxy evolution, dynamically reshaping the stellar and gas makeup of the hosts, as well as the galaxy-scale magnetic fields (B-fields) that permeate their interstellar medium (ISM), altering both B-field strength and configuration. In this work, we study in detail the link between the co-evolving gas and B-fields in the cold ISM phase of the nearby Antennae galaxy merger (NGC 4038/4039), using CO(3-2) data from the LAsMA receiver at the APEX telescope and ALMA, together with archival 154 $\mu$m dust polarization data from the SOFIA/HAWC+. We characterize trends between measurements of molecular gas intensity and velocity dispersion with polarization measurements at kpc-scales with the APEX and SOFIA data. We find anti-correlations with CO intensity ($\rho\lesssim-0.6$) and CO velocity dispersion ($\rho\lesssim-0.4$), which are signposts of geometric depolarization. Since dust polarization measurements are a density-weighted line-of-sight integrated average of B-field vectors below the beam size, the ALMA data can serve as a proxy of density structures below the SOFIA beam on a channel-by-channel basis. Through a geometric analysis of structures in the ALMA channels, we identify salient kinematic features in the overlap region strongly aligned with the SOFIA-inferred B-field orientation. This indicates that these kinematic features might dominantly lend their orientation to the measured field direction, assuming a correspondence between molecular gas structures and galactic B-fields, owing to their density or high polarization fraction. We also identify a modest trend ($\rho\sim0.5$) between the coherence of the molecular gas, a proxy of the line-of-sight angular dispersion, at small scales, and the SOFIA polarization fraction, showing that the polarization fraction measured at $\sim2$ kpc scales is sensitive to structures at $\sim50$ pc scales.

Griffiths phases provide a mechanism for sustaining critical dynamics over extended parameter regions without fine-tuning to an isolated transition point. This behavior has been mainly associated with structural heterogeneity in complex networks. Here we show that quenched heterogeneity in local parameters can generate a chaotic Griffiths phase in a network of map-based neurons. We study globally coupled Chialvo maps with quenched disorder in a local neuronal parameter, thereby isolating intrinsic parameter disorder from topological disorder. As the coupling strength is varied, a chaotic Griffiths phase emerges between desynchronized and coherent chaotic dynamics, characterized by intermittent formation and breakup of neuronal clusters and large fluctuations in the fraction of clustered neurons. Within this regime, cluster sizes exhibit power-law distributions with coupling-dependent exponents, providing evidence of critical behavior over a finite parameter interval. The chaotic Griffiths phase is absent in the homogeneous limit and broadens progressively as the degree of parameter heterogeneity increases. We further introduce topological disorder through small-world connectivity and find that its combined action with local parameter heterogeneity substantially extends the chaotic Griffiths phase. In both globally coupled and small-world networks, the number of positive Lyapunov exponents displays anomalous scaling with system size. These results demonstrate that intrinsic dynamical heterogeneity constitutes an independent mechanism for generating chaotic Griffiths phases and that its interplay with structural disorder enhances extended criticality in neuronal networks.

The physical conditions within the innermost few tens of au of high-mass protostars ($M_*>8~M_\odot$) remain largely unexplored. At these scales, ionised emission may trace photoionised gas, accretion disks, or the base of protostellar jets. We search for ionised emission in the form of radio recombination lines (RRLs) in high-mass star-forming regions. We use archival data from the Atacama Millimeter/submillimeter Array (ALMA) interferometer in band 6 ($\sim$1.3 mm) with a resolution of $\theta_\mathrm{beam}\sim53~\mathrm{mas}\times42~\mathrm{mas}$ to examine the high-mass protostar IRAS18162$-$2048, driving source of the Herbig-Haro (HH) objects HH80-81. We detect, for the first time in this protostar, the thermal emission of the RRL H30$\alpha$ ($\mathrm{rest~frequency}=231.90093$ GHz). The ionised emission is spatially unresolved in our observations, but we estimate an emitting region $\lesssim50$ au at the source distance of 1.4 kpc. The H30$\alpha$ line profile is broad and spectrally resolved with a Local Standard of Rest velocity of $\sim-14~\mathrm{km~s^{-1}}$ (i.e., $\sim-26~\mathrm{km~s^{-1}}$ blue-shifted with respect to the systemic velocity of the cloud) and a full width at half maximum of $\sim82~\mathrm{km~s^{-1}}$. These observations suggest that the H30$\alpha$ line is tracing outflowing blue-shifted material in the well-known radio jet from this object. This detection opens the possibility of detailed studies of the kinematics and physical properties of ionised jets.

S. Lie was the first to try to classify maximal subalgebras of simple vectorial (i.e., consisting of vector fields) Lie algebras with polynomial coefficients. In Appendix it is shown that, as stated, the problem is wild: describing all subalgebras contains the classification of pairs of matrices up to simultaneous conjugation, so there are too many such subalgebras. Several researchers distinguished various classes of maximal subalgebras important in applications. Here, in simple (or in Cartan prolongations close to simple) vectorial Lie superalgebras with polynomial coefficients considered with gradings associated with Weisfeiler filtrations, we describe maximal simple (and close to simple) graded subalgebras — a superization of what Sophus Lie tackled. Together with I. Shchepochkina's description of the maximal subalgebras of matrix Lie superalgebras, see arXiv:hep-th/9702122, this paper solves the super and infinite-dimensional version of Dynkin's problem — the description of the maximal subalgebras of simple finite-dimensional Lie algebras — except for the 3 exceptional ambients considered elsewhere.

Particle physics analysis depends on information retrieved from particle tracks in a detector. Traditionally, this involves collecting individual detector hits, recombining them to form tracks, and fitting them to a model function. Large Language Models (LLMs) have demonstrated remarkable proficiency in comprehending the grammar and semantics of languages. Our exploratory study leverages transformers to conceptualise particle tracking as a language translation problem, wherein the language of detectors is translated into the language of physics. A simple decoder-only architecture with a custom tokenizer was designed for this task, and the behaviour of the model in this atypical setting provided interesting insights. We evaluated the advantages of this formulation in terms of track-parameter accuracy, and the ease of representing multi-track events, and finally tested the model on real cosmic-muon data. On synthetic datasets, the model outperformed a simple regression technique by an order of magnitude in track-parameter accuracy while being on par with the random sample consensus (RANSAC) method. The model also identified multi-track events with an accuracy of 98%. The analysis of real cosmic-muon events showed an excellent agreement of the zenith angle distribution with expectations. Our results suggest that a compact language model shows competitive performance and a natural ability to represent multi-track events. Further studies with complex geometries are required to understand the full potential of this technique.

Yield point phenomena in metals, anticracks in soils and snows, or planewise collapses in lattice metamaterials, are different manifestations of plastic instabilities characterized by stress softening and strain localization. Several independent constitutive descriptions exist and suffer from similar challenges: experimental difficulties to link local events to global measurements, and computational lack of convergence, spurious localization and mesh sensitivity. This work proposes a minimal field-agnostic constitutive framework where plastic softening is represented by a damage-like internal variable which explicitly separates stable hardening behavior from unstable softening. The resulting formulation admits a variational structure and can leverage regularization tools originally developed by the ductile fracture community. The framework aims at emphasizing similarities within plastic instability phenomena and establishing a bridge with continuum damage mechanics, to open a path towards regularized variational formulations of localized plastic flows.

EOS is an open-source software package for a variety of computational tasks in flavor physics. In this article, we describe new developments for version 1.1 of this software and discuss changes with respect to version 1.0. These developments and changes include theory predictions for additional decay and scattering processes, such as semileptonic charm decays, hadronic tau decays, and $e^+e^- \to$ hadrons scattering. Overall, these changes have contributed 1063 additional (pseudo-)observables. Beyond the physics capabilities of the software, the changes include improving the quality and reducing the computational costs of posterior sampling; providing a more versatile and testable figure framework; and improving the overall user experience.

We propose multi-spin molecules as a viable architecture for fault-tolerant quantum computing. To this aim, we introduce a correction protocol handling both diagonal and off-diagonal errors, typically associated with dephasing and relaxation. The scheme is based on a hybrid encoding which combines dephasing-tolerant units suppressing the leading pure dephasing error into a multi-spin molecule implementing a multi-qubit code for residual off-diagonal errors. Our proposal leverages peculiar properties of molecular spins, i.e. the strong hierarchy between different errors and the possibility to engineer multi-spin molecules at the synthetic level. Moreover, it addresses the important issue of the loss of coherences in anharmonic systems subject to off-diagonal errors, which hampers their correction at the single spin level. Thanks to the huge suppression of dephasing by the first-level code, we numerically demonstrate the potential performance of this strategy even with a limited number of spins per logical unit.

Flow- and diffusion-based generative models have recently emerged as flexible and highly efficient forecasting models for dynamical systems. When combined with inference-time guidance, they offer a promising route to high-dimensional non-Gaussian data assimilation (DA), the problem of combining forecasts with observations to estimate latent system states. Existing filters, however, condition on a fixed history and assimilate only the most recent observation, leaving them unable to revise past states when new observations arrive. Estimates then stay tethered to a history that later observations may contradict, and errors accumulate over the assimilation run. To this end, we introduce **DAWIS**, a unified DA method covering filtering, fixed-lag smoothing, and block smoothing within a single framework. DAWIS replaces the single flow time of a state-level prior with a multitask stochastic interpolant over a window of consecutive states, assigning a separate flow time to each. An assimilation cycle inverts the window to a vector of per-state turning points and regenerates it under observation guidance, with the turning points controlling how strongly each state is held fixed, revised, or generated from scratch. The same construction can also absorb the forecast into the assimilation cycle, removing the need for a separate forecasting model. Experiments on challenging nonlinear systems show that DAWIS improves on both filtering and smoothing baselines under sparse, noisy, and nonlinear observations. The code for DAWIS is available at https://github.com/Erik-Wikingsson/DAWIS

Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution. While highly efficient for long-range prediction, this formulation can suffer from severe error accumulation when used with shorter time steps and does not explicitly encode the locality and temporal continuity of atmospheric dynamics. To address these limitations, we introduce **SDECast**, a Neural Stochastic Differential Equation (SDE) framework for continuous-time probabilistic weather forecasting. SDECast extends SDE Matching to learn stochastic dynamics directly in physical space, without requiring repeated SDE simulation during training. On a simulated geophysical flow, we show that SDECast recovers meaningful drift dynamics and faithfully reproduces the underlying continuous-time behavior. We then demonstrate its scalability to global weather forecasting at hourly resolution, where SDECast produces skillful probabilistic forecasts for lead times of up to five days.

Bell sampling efficiently learns the stabilizer group, and hence the stabilizer nullity, of pure states with little magic, a primitive underlying tomography and certification of low-magic states. Real devices, however, only prepare noisy images of the target, which we model as a Pauli channel acting on the ideal state; the resulting noisy state generically has no exact Pauli stabilizers. Existing structure learners for mixed states rely on such exact stabilizers. Agnostic tomography does not, but its guarantees scale exponentially in the nullity $t$. We show that the ideal stabilizer group remains encoded in the noisy state as the set of Pauli operators under which it is invariant. We give Bell-sampling algorithms that learn the group to accuracy $\varepsilon$ for states of nullity at most $t$. The difficulty is that noisy samples break the group structure pure-state learners rely on, so more samples can erase structure rather than reveal it. We resolve this with a finder-verifier scheme that extracts stabilizer structure from partially corrupted sample batches while certifying proposed updates. For sufficiently weak global depolarizing noise, both samples and runtime are $\mathrm{poly}(n,t,1/\varepsilon)$ when the channel's total error probability is at most of order $\varepsilon n/t$. Up to a constant total error probability ($\approx 0.16$), samples remain polynomial while classical processing costs $2^t$, and for any error probability below one a $4^t$ overhead suffices. Arbitrary Pauli channels exhibit the same three regimes, with more restrictive thresholds. For weak Pauli noise, this closes much of the gap between agnostic learners such as stabilizer bootstrapping and polynomial pure-state algorithms.