Papers

Quantum networks are transitioning from labo- ratory experiments to real-world deployments, with entangle- ment as their fundamental resource. Since an entanglement source effectively defines a quantum network, its performance directly impacts the reliability, scalability, and efficiency of future quantum communications. In this work, we investigate rack-mountable plug-and-play entangled-photon sources from a network engineering perspective, shifting the focus from device characterization to deployment-oriented performance evaluation. Building upon an extensive experimental campaign, we assess commercially deployable hardware across multiple operating conditions, evaluate current state-of-the-art capabilities, and pro- vide an outlook on future generations of entanglement sources. We identify and evaluate two key performance indicators (KPIs): multi-photon generation, capturing deviations from ideal single- pair emission, and entanglement quality, quantified through the reconstructed two-qubit density matrix. By combining the measured detected-pair rate with the one-way hashing bound derived from each density matrix, we estimate a lower bound on the achievable distillable-entanglement generation rate, providing a compact metric that captures the trade-off between pair throughput and entanglement quality. Finally, we translate these experimental results into lower bounds on the quantum-memory coherence time required for entanglement distillation, directly linking optical source performance to the hardware requirements of future quantum repeater nodes.

We study an extension of the Standard Model with two complex scalar singlets, $S_2$ and $S_3$, charged under $\mathbb{Z}_2$ and $\mathbb{Z}_3$, respectively, and a muon-philic vector-like lepton $\psi$ carrying the same $\mathbb{Z}_3$ charge as $S_3$. The pseudoscalar associated with the $\mathbb{Z}_2$ sector remains stable due to the CP symmetry of the potential and serves as the dark matter (DM) candidate, with its mass generated through the corresponding soft breaking of the global $U(1)$ symmetry. The vector-like muon couples to the $\mathbb{Z}_3$ scalar and renders the second pseudoscalar unstable, thereby realizing an effectively single-component pNGB DM scenario. The pNGB nature strongly suppresses the tree-level spin-independent direct-detection cross section, while viable parameter regions reproduce the observed relic abundance and satisfy LHC monojet constraints. We further compute the one-loop contribution to DM-nucleon scattering and find that the resulting cross section remains below current experimental limits while being potentially accessible to future direct-detection experiments. At a multi-TeV muon collider, scalar mediated $ t$-channel processes can significantly enhance vector-like-muon pair production. We perform a detailed multivariate analysis for a future muon collider at 3 TeV center-of-mass energy. We also identify viable benchmark points exhibiting strong first-order electroweak phase transitions (SFOEWPT) with successful bubble nucleation, which can generate potentially observable stochastic gravitational wave (GW) signals. These results highlight the complementarity of dark matter searches, muon-collider probes, and SFOEWPT, with the resulting GW signals providing an additional probe of the extended scalar sector.

Non-Markovian dynamics offer a new route towards engineering non-equilibrium matter, where memory and feedback act as programmable resources for controlling order in time. Here we report the realization of a non-Markovian spin oscillator in a hot vapour $^{129}$Xe-Cs co-magnetometer with programmable feedback delay and gain. By tuning these parameters, we observe a hierarchy of dynamical phases, including time-crystalline response, nonlinear bifurcations, and frequency-comb formation. The measured spectra and phase boundaries are captured by linear stability analysis of delayed Bloch equations, revealing these phenomena as different manifestations of the same memory-induced instability structure. These results establish time-delayed feedback as a powerful strategy for controlling non-equilibrium phases, enabling quantum sensing, frequency referencing, and synchronization within a single spin-based platform.

We study the statistical properties of passive and active many-body systems with orientational degrees of freedom on non-orientable domains. By rephrasing topological constraints as non-local symmetry relations on an orientable double-cover, we show that non-orientability eliminates global rotational soft modes without acting like an external field. In a passive XY model, this results in topological caging, where orientational fluctuations that exhibit conventional diffusive behavior on a torus saturate on a Klein bottle to a finite value that we compute exactly in the thermodynamic limit. In models of active self-propelled particles with orientational degrees of freedom, topological caging persists despite continuously changing interaction neighborhoods. In an active Ising spin model, non-orientability enforces the coexistence of ordered anti-parallel domains with vanishing global polar order, a state that is absent on orientable domains.

Massless particles, defined through Wigner's induced representations of the Poincaré group, are obtained as a limit of infinitely boosted massive particles wherein the massive little group undergoes an Inönü–Wigner contraction to the massless one. It is demonstrated that the spin-$s$ multiplet of a massive representation re-organizes into the direct sum of massless particles with helicities in the range $[-s,s]$. This group theoretic result is the Hilbert space counterpart of the Stückelberg formulation of massive fields of spin $s$ in the high-energy limit, where the Stückelberg fields become independent massless fields of spins $s'\leq s$ in integer steps. It therefore also provides the kinematic setting of the Goldstone equivalence theorem.

Deploying quantum networks over existing network infrastructures requires the same engineering foundations that underpin classical communications: quantitative models of the channel's noise and of the impairments it imposes on the transmitted information. In this work, we build such a foundation on experimental measurements, grounding the quantum-network counterparts of the two cornerstone metrics of classical link characterization - namely, the SINR and the BER - on a 7.3 km deployed metropolitan-scale fiber-loop interconnecting two campuses of the University of Naples Federico II within the national QuantumInternet.it testbed. On the noise side, we adopt a photon-counting quantum analog of the SINR - in which dark counts constitute the intrinsic noise and the photons generated by classical traffic (through either spontaneous Raman scattering or inter-fiber crosstalk) constitute the interference - and we quantify each contribution directly on the deployed loop. On the bit-error side, we consider the main degrees-of-freedom available to encode a quantum state within an optical photon - namely, polarization, time, and frequency - and we quantify for each degree the channel-induced degradation and its drift over time. These results show that a quantum fiber link, like its classical counterpart, can be captured by a small set of measurable parameters, turning quantum networking over deployed fiber from a physics demonstration into an engineering design problem. Together, they provide the key ingredients of a quantum link budget for the Quantum Internet.

Analog quantum computers provides direct access to continuous many-body dynamics, but their native control Hamiltonians generate only a restricted operator space. Consequently, the fidelity with which they can reproduce a target Hamiltonian's dynamics depends not only on spectral agreement but on whether the physical controls can actually generate the required evolution. We introduce a geometry-independent framework for diagnosing and optimizing this compatibility between the spectral algebra generated by a target Hamiltonian $H$ and a Krylov-type operator space generated from the device's independently tunable control Hamiltonians and a fixed initial state. The encoding problem can be formulated as an optimization over the unitary orbit of the target Hamiltonian. We maximize a smooth subspace-overlap functional using Riemannian gradient descent on $U(d)$, thereby selecting a spectrally equivalent representation whose target algebra is better aligned with the native controls. We apply this framework to the deuteron Hamiltonian encoded on a Rydberg-atom analog processor. The standard binary encoding is strongly misaligned with this space for small system sizes, while principal-angle optimization substantially improves the algebraic compatibility of the encoding. Moreover, using a single geometry-independent optimized encoding substantially reduces the sensitivity of state-preparation fidelity to the atomic geometry. These results establish algebraic reachability as a useful preprocessing criterion for analog encoding design: it identifies representation-level incompatibilities before device-specific geometry and pulse optimization, while the distinction between algebra-level and state-level reachability clarifies when full encoding optimization is necessary and when geometry-dependent control resources can compensate for incomplete algebraic alignment.

How does continuum operator structure appear in a finite qubit register? We address this question for analytic functions of diagonal operators represented in a binary basis, focusing on the Jordan–Lee–Preskill (JLP) momentum operator, by developing an operator-level generating-function framework for their Walsh-Pauli spectra. The construction determines the spectrum analytically and reveals exact support and parity selection rules, together with a nontrivial intra-shell hierarchy induced by the binary encoding. This provides a systematic characterization of Walsh compressibility and its relation to Pauli locality. As a controlled application, we consider generalized uncertainty-principle (GUP) kinematics as a tunable nonlinear odd deformation of momentum, showing how higher-order momentum terms redistribute spectral weight into progressively higher Pauli-weight sectors

Altermagnets (AMs) exhibit momentum-dependent spin splitting without net magnetization, making them promising platforms for spintronic applications. While the symmetry and multipolar origins of nonrelativistic altermagnetic spin splitting (NRASS) are well established, the role of electronic correlations and spin-lattice coupling (SLC) in controlling NRASS remain unexplored. In particular, SLC provides a direct pathway for tuning altermagnetism through lattice degrees of freedom. Here we first investigate the role of electronic correlations on altermagnetic strength in NiAs-type material CrSb using density functional theory (DFT), DFT+$U$, and dynamical mean-field theory (DMFT). We find that increasing electronic correlations substantially enhance NRASS and drive the stabilization of an incommensurate spin-spiral (SS) state, while dynamical correlations further amplify the spin splitting through quasiparticle renormalization of the Cr-$3d$ states. We find that the NRASS remains 12.5% larger in DFT+DMFT than in DFT. By evaluating SLC in next step, we establish its direct connection to the evolution of NRASS and identify SLC as a microscopic descriptor between electronic correlation and enhanced altermagnetism in CrSb. Investigating it in another NiAs-type compound MnTe, we show that the SLC-NRASS correlation is generic across AMs and establish SLC as a microscopic descriptor of altermagnetic strength. Our results provide a unified framework for tuning altermagnetism through the interplay between the electronic correlations and lattice degrees of freedom.

We propose a novel theoretical architecture for implementing a quantum random access memory (qRAM) based on quantum random walks in a Rydberg blockaded atomic ensemble utilizing multilevel Electromagnetically Induced Transparency (EIT). Unlike previous approaches that rely on geometric phase gates in solid-state or trapped ion systems, our scheme harnesses the strong, coherent dipole dipole interactions between Rydberg atoms to achieve high-fidelity phase control of photonic qubits without the need for cryogenic temperatures. By generating conditional phase shifts through cross-phase modulation in multiple lambda-type EIT systems, we realize the controlled unitary operations requisite for an efficient qRAM. In the proposed architecture, Zeeman splitting is used to engineer a set of parallel lambda systems in a cavity, where pairs of magnetic sublevels of the ground state are coupled to highly excited Rydberg states via circularly polarized laser pulses. These Rydberg excited EIT systems serve as the elementary phase gates that form the nodes of a binary tree enabling quantum random walking. Address and data qubits are encoded into distinct probe fields and coherently mapped into the metastable atomic states, where their interactions within the EIT medium generate conditional phases required for state-selective routing. The system uses $n+m$ layers of cold alkali atoms to form an $n$-level binary tree of Rydberg nodes connected to $2^n$ cavity-trapped memory atoms, operated by $n+m$ laser pulses acting as quantum walkers and address units. Our scheme offers a scalable, reducing operational complexity to $\mathcal{O}(n)$ and highly coherent pathway toward photonic qRAM, exploiting collective Rydberg interactions to realize programmable, parallel entangling operations in an atomic ensemble.

Open chemical reaction networks exhibit stochastic concentration dynamics at finite system sizes, whereas their macroscopic limit is governed by deterministic rate equations that can display chaos. In this Letter, we show theoretically that a rate of information loss constructed from two-time mutual information recovers the Kolmogorov-Sinai entropy in the deterministic limit. We verify this result through numerical simulations of a Markov jump process for a three-species system involving seven reactions.

Statistical physics extracts macroscopic laws by averaging over the many microscopic degrees of freedom of a system. Disordered systems demand a second and far harder average, one over the quenched randomness itself. The classic analytical routes, the replica and cavity methods, become uncontrolled outside mean-field or tree-like limits, and conventional numerical algorithms like parallel tempering require expensive, independent equilibration for every disorder realization. In this work, we introduce a universal neural variational framework that amortizes inference across the disorder ensemble, eliminating both the need for per-instance Markov chain equilibration and the cost of retraining instance-specific variational ansatzes. Built on an encoder-decoder Transformer architecture, after training once, it produces an explicit approximation to the Boltzmann distribution given previously unseen disorder realizations without further optimization. We validate this framework on 2D Edwards-Anderson models, and apply it to the random-bond Ising model, successfully capturing the Binder cumulant crossings near the Nishimori multicritical point. These results shift the object of variational inference from the single instance to the disorder ensemble, opening a route to frustrated many-body systems where instance-by-instance computation is prohibitive.

Quantum random access memory (qRAM) is crucial for overcoming data-loading bottlenecks in quantum machine learning; however, current physical implementations face severe scalability constraints. Traditional fanout designs demand exponential decoherence-prone gates, while bucket-brigade schemes require highly error-prone active switches. Motivated by these limitations, we propose a scalable qRAM architecture that fundamentally replaces active nodes with phase-encoded quantum walkers. Our methodology maps a discrete-time quantum walk onto a cavity quantum electrodynamics framework utilizing an electromagnetically induced transparency (EIT)-based Rydberg atomic ensemble. Inside hollow-core waveguides, strong Rydberg dipole-dipole interactions and a solenoidal magnetic field create a robust routing operator. This operator imparts precise, polarization-dependent phase shifts, steering circularly polarized probe pulses to target memory cells. Our results demonstrate that operating within a strong control field regime suppresses emergent spatial attenuation, ensuring cumulative transmission probabilities for highly scaled memory addresses. Ultimately, this parallelized architecture successfully optimizes spatial resources to static gates and temporal complexity to an optimal logarithmic scale of $\mathcal{O}(n\log(n+m))$ by requiring $\mathcal{O}(n+m)$ physical walkers, establishing a practical, fault-tolerant hardware pathway for advanced quantum computation implementations.

We constrain the particle mass of the fuzzy dark matter model using globular cluster candidates in dwarf galaxies, from the Euclid Early Release Observations, within the Fornax and Perseus galaxy clusters. We model the orbital evolution of globular clusters by combining fuzzy dark matter density profiles with the corresponding dynamical friction formalism, and integrate their orbits using galpy. In the fuzzy dark matter framework, both the halo density profile and the dynamical friction force are modified by the wave-like nature of the ultra-light dark matter particle, whose mass is parameterized as $m_{22}=m_\chi/10^{-22},\mathrm{eV}$ and explored over the range $0.1 \leq m_{22} \leq 100$. This framework naturally alleviates the long-standing globular cluster timing problem originally identified in the Fornax dSph. We find that globular clusters no longer experience orbital decay toward the centers of their host galaxies for $m_{22}\leq1.46^{+0.51}_{-0.32}$ in the Fornax cluster and $m_{22}\leq1.09^{+0.14}_{-0.21}$ in the Perseus cluster. Future Euclid data releases, providing substantially larger samples of globular cluster candidates, will enable significantly tighter constraints on the dark matter particle mass within the fuzzy dark matter scenario.

Quantum neural networks are a prominent model of quantum machine learning. Their training consists in the minimization of a given loss function over a parametrized family of quantum circuits, mathematically described by unitary operators, or, more generally, completely positive linear maps. In this work, we extend the notion of quantum neural network, using random sampling and classical data processing to enlarge the optimization space in a way that includes linear combinations of completely positive maps. Our extended model, called virtual quantum neural networks, leverages its enlarged optimization space to achieve increased expressivity and improved noise robustness. These benefits are illustrated in three representative tasks: quantum error mitigation, binary classification, and estimation of ground-state energies. Overall, virtual quantum neural networks offer a flexible learning paradigm that expands the space of achievable computations and strengthens the applications of near-term quantum hardware.

One-halo galactic conformity is the tendency for satellites in halos with quenched centrals to have lower star-formation activity than those in halos with star-forming centrals at fixed halo mass. It is an important probe of the galaxy–halo connection and halo-wide quenching processes that may couple central and satellite evolution. However, its existence remains controversial, because conformity must be measured at fixed halo mass, while halo masses are difficult to estimate accurately. In this Letter, we measure one-halo conformity in SDSS using five stellar-mass-complete samples and three halo-mass estimates: an ML estimate whose star-forming and quenched stellar mass–halo mass relations (SHMRs) agree with independent weak-lensing constraints, and two conventional abundance-matching (AM) estimates. We quantify conformity as the difference in median $\log({\rm sSFR})$ between satellites of star-forming and quenched centrals, using both satellite-level and halo-level statistics. The two AM estimates produce strong positive conformity signals, consistent with previous AM-based measurements, but these signals are not reproduced with the ML halo masses. For the halo-level statistic, the representative AM-based signals are $+0.38\pm0.04$ dex and $+0.23\pm0.04$ dex for the luminosity-ranking and mass-ranking AM halo masses, detected relative to no conformity at about $10\sigma$ and $6\sigma$, respectively. In contrast, the ML result is consistent with no conformity, $+0.00\pm0.03$ dex; the satellite-level statistic gives a similar result. Thus, with halo-mass estimates consistent with weak-lensing constraints, we find no detectable one-halo conformity signal in the present SDSS sample, suggesting that the strong AM-based signal is largely driven by halo-mass estimation biases.

HfO$_2$ exhibits rich polymorphism, and competition among different phases underpins many of its functional properties. Yet bulk free-energy relations alone cannot explain phase selection at mixed-phase boundaries, where interface orientation and structural continuity constrain collective rearrangements. Here, using machine-learning atomistic simulations and a Hf-centered local phase classification scheme, we show that crystallographic interface matching redirects phase competition and accessible transformation pathways. The M(100)/T(100) interface remains pinned as an M/T mixture throughout 3 ns simulations from 300 to 1800 K. M/PO, M/AO, and PO/AO interfaces retain two-phase coexistence up to 900 K, whereas all T/PO interfaces become PO-dominant. At 1800 K, all non-M interfaces become T-dominant, while M-containing interfaces retain a monoclinic majority. Nudged elastic band calculations reveal lower-barrier routes through interface states. For M(100)$\rightarrow$T(100), the interface-mediated route in a long cell ($\sim$12 nm) yields a barrier of 136.03 meV/f.u., 29.2 % lower than the direct route in a short cell ($\sim$3 nm). This difference is associated with sequential phase-front motion absent from the short cell. These results identify phase boundaries as active participants in phase stability and transformation and establish interface orientation and crystallographic matching as variables for stabilizing metastable polymorphs and directing phase conversion in HfO$_2$.

Initial spatial clustering of primordial black holes (PBHs) induced by local-type non-Gaussianity (LNG) can substantially modify cosmological constraints on PBH abundance. Several inflationary scenarios that enhance curvature perturbations at small scales relevant to PBH formation predict LNG that is not necessarily perturbative. Therefore, it is crucial to establish a theoretical framework capable of investigating the initial clustering induced by arbitrary LNG. Here, we present a general analytical formulation for the PBH two-point correlation function applicable to arbitrary LNGs in the alternative approach. Under the assumptions that PBHs form only at the large peaks of perturbations, and that large-scale modes weakly modulate the local variance of small-scale perturbations, we derive an analytic expression for the PBH bias parameter, directly connecting initial clustering to the primordial trispectrum in the collapsed limit. We demonstrate the versatility of our formula by computing the bias parameters in the ultra-slow-roll inflation, curvaton, and modulated reheating scenarios. We also formally generalize the framework to broad power spectra to account for correlations across different PBH mass scales. Because our formulation does not rely on weak or perturbative non-Gaussianity assumptions, our result provides a universal theoretical basis for evaluating initial clustering impact on PBH observables.

We investigate whether dimensional deconstruction can qualitatively modify long-range forces. We show that the deconstruction of a five-dimensional AdS spacetime effectively generates an inverse-square potential, which naturally gives rise to broad p-wave Sommerfeld resonances without fine-tuning. We then apply this mechanism to dark matter phenomenology.

We propose an all-reflective two-mirror projection system for extreme ultraviolet (EUV) lithography operating at exposure wavelengths of $13.5$~nm (Mo/Si) and $11.2$~nm (Ru/Be), delivering a fourfold ($4\times$) demagnification of the periodic mask pattern at a numerical aperture approaching unity ($\mathrm{NA}_{\max} \approx 0.993$). In contrast to conventional EUV projection objectives that incorporate 6–10 aspheric mirrors with an overall optical throughput of less than $15\%$, the proposed design redirects each accepted discrete spatial diffraction order scattered by the mask onto the wafer via a dedicated pair of planar mirror facets. The number of reflections is strictly fixed at two for all accepted orders, retaining $50$–$60\%$ of the power leaving the mask in each accepted order. We derive a spatial geometry providing rigorous optical path length equalization across all diffraction orders, thereby removing order-dependent propagation phase shifts. Individually optimized 30-bilayer Bragg multilayer coatings are designed for each facet using the transfer matrix method combined with global evolutionary optimization algorithms. The architecture is generalized to a three-dimensional vector formulation with a two-dimensionally periodic mask. Utilizing inverse lithography technology, Fourier parameterization, and a differentiable electromagnetic modal waveguide solver, we solve the synthesis problem for binary absorber masks (La absorber on a Ru/Be/Sr multilayer mirror). We demonstrate simulated aerial images of sub-10-nm features on the wafer (isolated peaks with a full width at half maximum (FWHM) of approximately $5.4$~nm and line pairs with a critical dimension of $6$~nm) and find that the two peaks remain resolved for the tested wafer defocus values from $0$ to $5$~nm along the $z$-axis.

We review recent progress on a model for the relativistic electron-positron field interacting with a classical electromagnetic field at positive temperature. The ultimate goal is to advance our comprehension of how thermal backgrounds affect polarisation mechanisms of this quantum system.

We revisit the role of the trace sector of the graviton in covariant string amplitudes by constructing the ghost-number-three graviton vertex operator via BRST descent, imposing only the de Donder gauge condition without requiring tracelessness. The resulting operator is well defined even at zero momentum, while for nonzero momentum the trace-dependent sector of the descent chain forms a trivial BRST descent chain. Evaluating its disk one-point function in the presence of a D$p$-brane, we show that the trace-dependent term is essential for reproducing the correct graviton-brane coupling, canceling the Dirichlet-direction contributions and leaving the expected coupling to the world-volume components.

Driven dynamical systems can compute when their transient states encode complex transformations of past inputs. The information processing capacity (IPC) framework allows for a detailed accounting of these computational properties, however its interpretation in noisy systems has remained incomplete. In this work, we clarify how noise affects the IPC and how one can reconstruct the noiseless IPC. First, we show how to distinguish the dynamics of an unperturbed system from the noise-free component of the stochastic dynamics: The IPC measured for responses averaged over noise realizations is in general not the same as the IPC of the unperturbed system. We explicitly demonstrate that noise can redistribute computational capacity and sometimes even enhance performance on particular tasks, rather than merely degrading a fixed computation. We then introduce covariance reconstruction by orthogonal projection (CROP), which reconstructs the covariance and IPC of the noise-free component directly from noisy observations, without requiring a detailed model of either the system or the noise. At fixed total measurement budget, numerical tests on a classical nonlinear reservoir show that CROP estimates the noise-free IPC more accurately than the standard practice of ensemble averaging over repeated trials. We find the same advantage in a quantum reservoir subject to unavoidable measurement noise. Our results provide a general route to recovering the computational structure of noisy physical systems from finite observations.

Scientists constantly face decisions about what lines of research to pursue. This Element introduces the philosophical debate about scientific pursuitworthiness. It explains how it can be rational to pursue a theory even if the theory is less well supported than its rivals, and it discusses existing philosophical frameworks for guiding pursuit decisions. The Element also develops a new perspective. Existing accounts focus predominantly on theories, while experiments are largely neglected. This is an important shortcoming. Theoretical promise depends on experimental promise, and experimental promise raises questions of its own. Drawing on the epistemology of experimentation, the Element advances an account of experimental pursuitworthiness. It is argued that such pursuitworthiness depends on experimental virtues like a clear signal and simplicity of design. Moreover, the kinds of uncertainty that constrain the assessment of scientific pursuits are examined. Finally, the Element highlights open questions in the philosophy of scientific pursuitworthiness.

In the strongly interacting massive particle (SIMP) scenario, dark matter is a pseudo-Nambu–Goldstone boson whose abundance is set by a three-to-two Wess–Zumino–Witten process. We study this scenario in an axion portal and find that the kinetic contact the canonical mechanism assumes is excluded by bounds on sub-GeV axion-like particles (ALPs). The dark sector then freezes out at its own temperature, so the relic abundance no longer fixes the self-interaction cross section but predicts the portal coupling instead. Moreover, the portal operator is Hermitian and even in the ALP field, so no trilinear ALP–pion coupling arises. The contact term then drives $\pi\pi\to aa$ with nothing to cancel against it, and we find the conversion four orders of magnitude faster than a trilinear estimate gives. This disfavours the minimal realisation in which the dark condensate alone generates the ALP mass. The realisation that survives instead makes the ALP slightly heavier than the dark pion. Matching the observed abundance then fixes the flavon vacuum expectation value at $V_\phi\simeq 1.1\times10^{10} \ {\rm GeV}$ and leaves the dark scale open over more than an order of magnitude. The decay $K^+\to\pi^+a$ requires the flavon to charge the leptons alone. The model then predicts a dark matter self-interaction of $0.20\,{\rm cm^2/g}$ at a dark pion mass of $140\ {\rm MeV}$, in a window running from $82$ to $169 \ {\rm MeV}$. The predictive power of the SIMP framework is therefore not lost but relocated, from the self-interaction to the flavon scale.

A three-dimensional multiphysics finite element model for the simulation of high-frequency induction welding of tubes is presented. The model couples a time-harmonic $h-\phi$ formulation with a stabilized quasi-stationary advection-diffusion heat transport equation, enabling accurate prediction of electromagnetic and thermal fields under industrial operating conditions. The model incorporates parameterized geometry generation and semi-automated, physics-tailored mesh construction and is implemented using the open-source tools GetDP and Gmsh. Validation against measurements from a commercial induction welding line for AISI 304 stainless steel tubes demonstrates good agreement with operating data. The validated model is subsequently applied to investigate the influence of impeder material by comparing a conventional FeNiZnV ferrite with the soft magnetic composite Ferrotron 559H for the induction welding of AISI 304 stainless steel tubes.

Reinforcement learning (RL) can automate quantum architecture search, but its scalability is limited when useful circuit trajectories become rare in the rapidly expanding search space. Existing replay mechanisms reuse observed transitions; the proposed learned model produces additional predicted one step transitions from real state-action seeds. Here we introduce GenQAS, a tensor network-guided RL framework that combines a fixed matrix product state warm-start with prioritized generative replay. A learned local transition model generates synthetic circuit transitions on demand and mixes them with real experience during Double Deep Q-Network updates. Under a random exploration analysis, near ground state circuits occupy a rapidly shrinking region of the accessible state space. We investigate whether real data anchored synthetic replay can improve the effective training signal in this regime. Across chemical Hamiltonian benchmarks from 6 to 12 qubits, GenQAS improves fixed-budget success probability and identifies compact circuits at competitive energy error. At 12 qubits, it improves final success probability by up to $7.0\times$ over passive replay. On a 15-qubit transverse field Ising model, GenQAS increases success probability from $12\%$ to $21\%$. In a noisy 6-qubit BeH$_2$ transfer experiment, generative replay reduces the steps to chemical accuracy by $92.7\%$. These results show that generative replay can mitigate sample starvation in quantum architecture search and support more resource efficient circuit discovery.

We realize a nearly pure higgsino dark matter candidate with mass of about 1 TeV in the Minimal Supersymmetric Standard Model coupled to the Starobinsky supergravity. The recent LUX-ZEPLIN 248 keV nuclear-recoil event has renewed interest in higgsino dark matter. In this framework, the particle spectrum, including the Higgs boson mass, the higgsino mass and its splitting, is connected to cosmic inflation observables via gravitational mediation of supersymmetry breaking and subsequent renormalization group evolution. We show that the Higgs boson mass predicted by the model agrees with the measured value within the quoted uncertainties for a broad range of bino-induced mass splittings. Wino-induced splitting leads to a Higgs mass several GeV higher and is therefore excluded.

Understanding whether electronic and structural order remain coupled under nonequilibrium conditions is a central challenge in correlated materials. Here, we simultaneously track metallicity and symmetry across the photoinduced insulator-to-metal transition in NdNiO$_3$ using time-resolved optical reflectivity and symmetry-sensitive second-harmonic generation. We find that metallic reflectivity emerges at significantly lower excitation fluence than restoration of the orthorhombic high-temperature symmetry. As a result, optical excitation stabilizes a metastable state that combines the reflectivity of the metallic phase with the monoclinic symmetry of the insulating phase, revealing an optically induced monoclinic metal. Only at substantially higher fluences does the symmetry fully recover to that of the high-temperature phase. These results demonstrate a nonequilibrium decoupling of metallicity and structural symmetry and establish simultaneous multiprobe spectroscopy as a powerful approach for identifying emergent phases in correlated materials.

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.