Papers

The vanadium-based kagome superconductor CsV$_3$Sb$_5$ exhibits an intricate interplay between charge density wave (CDW) and superconductivity (SC). Substituting Ta for V will suppress the CDW order and enhance superconductivity. Here, we report a high-pressure study up to 48 GPa on Ta-doped Cs(V$_{0.86}$Ta$_{0.14}$)$_3$Sb$_5$ with enhanced $T_\mathrm{c}$ around 5.29 K and without CDW order. At low pressures, unlike the characteristic $``$M-shaped" temperature-pressure phase diagram of the pristine CsV$_3$Sb$_5$, the Ta-doped compound exhibits a distinct non-monotonic evolution. Initially, $T_\mathrm{c}$ drops slightly from 5.29 K at ambient pressure to 4.90 K at 0.33 GPa, before increasing to a maximum of 5.70 K at 0.61 GPa. Upon further compression, $T_\mathrm{c}$ decreases continuously to 0.98 K at 12.3 GPa. At higher pressures, $T_\mathrm{c}$ rises again, reaching 4.46 K at 47.7 GPa. This evolution of $T_\mathrm{c}$ establishes two superconducting domes under pressure, which are similar to those in CsV$_3$Sb$_5$. Our results demonstrate that pressure-induced double-dome superconductivity is intrinsic to these V-based kagome superconductors, even in the absence of long-range CDW order.

Superconducting quantum circuits underpin large-scale quantum processors and are increasingly interfaced with optical systems, yet they are vulnerable to quasiparticle poisoning from high-energy radiation and optical fields. Gap engineering of Josephson junctions suppresses quasiparticle-induced relaxation, but quasiparticles near the junction can still shift the qubit frequency and cause phase errors. Here, we extend gap engineering beyond the junction using a flux-tunable transmon with a gold-on-niobium ground plane and island and strongly gap-engineered aluminum junctions designed to keep quasiparticles away from the junction leads. Compared with an all-aluminum transmon with nominally identical junctions, we demonstrate that the niobium device withstands more than an order of magnitude higher near-infrared optical power before excess relaxation appears. The Nb transmon shows no resolvable quasiparticle-induced frequency shift over the same power range. At the same time, we observe that dephasing and state-preparation-and-measurement infidelity increase with optical power even while relaxation remains protected. These results suggest that engineering the quasiparticle energy landscape beyond the junction can substantially increase qubit resilience, not only to radiation-induced error bursts, but also to nearby optical fields.

Non-Abelian gauge theories above four dimensions may admit an interacting ultraviolet fixed point through the competition between canonical scaling and gauge-field antiscreening. Using the four-loop $\overline{\mathrm{MS}}$ beta function continued to $D=4+2\epsilon$, we determine the largest dimension $D_{\mathrm{pt}}=4+2\epsilon_{\mathrm{pt}}$ below which successive loop corrections to the fixed point remain hierarchically ordered under a specified ratio criterion, and map $D_{\mathrm{pt}}$ over color and flavor numbers for fermions in the fundamental representation of $SU(N)$. At $D=5$, we construct rational anomalous dimensions with a fixed, pole-regularized denominator inspired by the functional renormalization group (fRG). Matching to the $\overline{\mathrm{MS}}$ series through one, two, and three loops produces progressively smaller shifts in the critical flavor number and coupling at the ultraviolet–infrared merger (the point where the infrared and ultraviolet fixed points coincide), yielding $N_f^{\mathrm{cr}}\simeq 2.09 \, N$ at large $N$ in the three-loop construction. This low-order stabilization, together with qualitative evidence from fRG flows including higher gauge operators, suggests a potential merger scenario that bounds the candidate asymptotically safe region in flavor number. The resulting color–flavor map provides guidance for five-dimensional model building by displaying both the baseline perturbative-control boundary and the candidate three-loop merger boundary. Four-loop matching, however, retains an interacting ultraviolet fixed point beyond the three-loop merger boundary, with no merger found for integer $2\le N\le15$ and $0\le N_f/N\le11/2$. The sensitivity of the fixed-point structure to higher-order terms leaves both scenarios open, highlighting fixed-point annihilation as a potential constraint on five-dimensional asymptotic safety.

A recent experiment [arXiv:2507.08795] has reported measurements of the interwire $X$-parity lifetime in a Majorana tetron device via the quantum capacitance of an auxiliary readout dot. Motivated by these measurements, we theoretically study the interwire parity lifetime in a minimal model of the coupled dot-tetron system subject to electrostatic noise. We show that coupling to the measurement dot can itself strongly modify the parity dynamics by generating an energy splitting that shifts noise-induced transitions to finite frequency and thereby stabilizes the measured states against low-frequency fluctuations. For representative noise strengths motivated by disordered nanowire devices, we find $X$-parity switching lifetimes of order microseconds in the measurement configuration even when the corresponding bare-tetron coherence time is only tens of nanoseconds long. We analyze both the dispersive regime, where the dot can be integrated out and the dynamics reduces to an effective two-level system, and the near-resonant regime, where all four low-energy states participate. In the latter case, rapid transitions organize the spectrum into two long-lived manifolds that approximately correspond to sectors of the combined dot-tetron $X$-loop parity, yielding effective two-state switching despite the underlying multilevel structure. Our results show that measurement-induced stabilization can substantially enhance the observed $X$-parity switching lifetime, such that the lifetime measured under continuous readout can greatly exceed the intrinsic idle $T_2^*$ coherence time of the isolated Majorana tetron qubit.

Quantum networking represents the future of secure communication, enabling fundamentally new capabilities through quantum information exchange. However, these networks require secure, high-fidelity quantum communication channels, and they require every participating node to be a genuine quantum device. Currently, there is no reliable method to verify whether clients entering a quantum network truly possess quantum computational capability and are connecting over robust channels. In this work, we introduce a client-validation protocol that functions as a proof-of-quantum-capability handshake. Our protocol secures a quantum network against classical spoofers, noisy quantum computers, and genuine quantum devices connecting with the network over noisy or insecure channels. We validate the efficacy of this protocol through numerical simulations under erasure, dephasing, and depolarizing noise models.

We present a combined analysis of the latest publicly available atmospheric neutrino data from Super-Kamiokande including all data-taking stages previous to the gadolinium phase, IceCube-DeepCore, and the six-detection-unit configuration of KM3NeT/ORCA. Our fit uses all event samples simultaneously and treats the systematic uncertainties shared among experiments as fully correlated, while detector-response uncertainties are modeled independently for each experiment. %We measure The combined data constrain the atmospheric oscillation parameters to $\Delta m^2_{31} = 2.44^{+0.06}_{-0.04} \times 10^{-3}\,\mathrm{eV}^2$ and $\sin^2\theta_{23} = 0.52^{+0.03}_{-0.04}$, with a precision of $2.2\%$ and $6.1\%$, respectively, which is competitive with current accelerator-based measurements, and independently constrain the $CP$-violating phase to $\delta_{CP} = 1.30^{+0.24}_{-0.28}~\pi$. Finally, comparing the orderings of the neutrino mass spectrum, we find a preference for the normal ordering with $\Delta\chi^2 = 11.0$; further calibrated with pseudoexperiments, we exclude the inverted ordering at $99.95\%$ confidence level ($3.3\sigma$).

GW231123 is the most massive binary black hole merger observed to date and one of the most intriguing hierarchical merger candidates in the pair-instability mass gap. Its exceptionally large inferred component spins and remarkably short in-band duration make the source highly sensitive to waveform physics near merger. Here we show that GW231123 is better described as an eccentric binary black-hole merger. We analyse data over 11–448 Hz using the eccentric, spin-precessing effective-one-body waveform model TEOBResumS–Dalí, accounting for detector calibration uncertainty. We measure $e_{5.5\,\mathrm{Hz}}=0.34^{+0.12}_{-0.23}$ at 90% credibility and obtain a Bayes factor of 10.2 in favour of the eccentric model over its quasi-circular counterpart. GW231123-like injection–recovery studies further show that analyses starting at 20 Hz, as in previous studies, can obscure this eccentricity. Including eccentricity substantially changes the spin inference, weakening the preference for a near-extremal secondary spin and lowering its posterior median to $a_2\simeq0.7$, close to the characteristic spin expected for a black-hole merger remnant. These results provide a coherent dynamical interpretation of GW231123, in which residual eccentricity, a mass-gap black hole, and a remnant-like spin arise naturally from a hierarchical merger channel in a dense environment.

For $s>0$, and $z\in\mathbb H=\{z=x+iy:y>0\}$, define $\theta(s;z)=\sum_{m,n\in\mathbb Z}e^{-\pi s\big(m^2y+\frac{(mx-n)^2}{y}\big)}$ and $ J(z;a,b)=\sum_{m,n\in\mathbb Z}e^{-\pi \big(m^2y+\frac{(mx-n)^2}{y}\big)} \cos\bigl(2\pi(ma+nb)\bigr)$ for the classical and shifted theta functions (Gaussian lattice sums), respectively. We prove that, up to the modular group, there exist three thresholds $0<\alpha_a<\alpha_b<\alpha_c<1$ such that \begin{equation}\aligned\nonumber \Minima_{z\in\mathbb H,\,(a,b)\in\mathbb R^2}\Big(\theta(1;z)+\alpha J(z;a,b)\Big)= \begin{cases} \;\big(e^{i{\pi}/{3}};1/3,1/3\big), &\hbox{if}\;\; \alpha\in[0,\alpha_a),\\ \;\big(e^{i{\pi}/{3}};1/3,1/3\big)\;\hbox{or}\;\big(e^{i{\varphi_{\alpha_a}}};1/2,1/2\big), &\hbox{if}\;\; \alpha=\alpha_a,\\ \;\big(e^{i{\varphi_\alpha}};1/2,1/2\big),\;\varphi_\alpha\in(\varphi_{\alpha_a},\frac{\pi}{2}),\;\; &\hbox{if}\;\; \alpha\in(\alpha_a,\alpha_b),\\ \;\big(i;1/2,1/2\big),\; &\hbox{if}\;\; \alpha\in[\alpha_b,\alpha_c],\\ \;\big(iy_\alpha;1/2,1/2\big),\; y_\alpha\in(1,\sqrt3],\;\;y_1=\sqrt3,\;\;&\hbox{if}\;\; \alpha\in(\alpha_c,1], \end{cases} \endaligned\end{equation} where the complex variable $z$ and vector $(a,b)$, represent the total vortex shapes and relative positions of the two-component Bose gas, respectively. Consequently, this completely proves the conjecture of Mueller and Ho~\cite{Mue2002} (2002) arising in Bose–Einstein condensates.

Autocorrelation in black hole movies encodes the time delays and angular separations between gravitationally lensed images, offering a means of testing the spacetime geometry. Using an orbiting hot spot in the parametrized Johannsen-Psaltis metric with a single deformation parameter, we investigate the ability of lensing-induced correlations to distinguish departures from Kerr. From ray-traced movies, we extract the slope (k) and intercept (b) of an approximately linear secondary ridge in the time-angle autocorrelation map as potential future observables. Variations in the spin and the metric deformation parameter produce distinctive trajectories in the (k,b) plane, allowing the two observables to jointly constrain both parameters when the orbital radius, observer inclination, and orbital angular velocity are specified. By varying the orbital angular velocity, we construct a Kerr reference region in the (k,b) plane, and we find that, for some low-spin configurations with negative deformation parameters, the measured slope-intercept pairs of the secondary peaks lie outside this reference region. This feature appears for sub-Keplerian, Keplerian, and super-Keplerian motion, indicating that secondary-ridge measurements from black hole flare movies have the potential to distinguish non-Kerr signatures.

A band that is exactly flat at zero magnetic field has zero velocity, a divergent effective mass, and no Fermi surface, so the standard theory of orbital magnetism does not apply. We show that the magnetic field generates an effective dispersion. In the expansion organized by the Moyal algebra of the noncommutative kinetic momenta, the band energy acquires a first-order shift $B\,\mathcal{M}(\mathbf k)$, set by the orbital moment of the Bloch state, and a second-order shift $\tfrac12\mathcal{X}(\mathbf k)B^{2}$, whose gauge-invariant coefficient we obtain in closed form. These two functions, together with the Berry curvature $\Omega$, fix the entire orbital response. The susceptibility is purely geometric, and for a generic isolated flat band it reproduces the established gauge-invariant response formula. With the chemical potential pinned to the flat band, the $\mathcal{M}=0$ contour becomes an emergent Fermi surface, and bosonizing excitations in its vicinity yields a modified Lifshitz–Kosevich formula with level spacing quadratic in $B$. The phase shift of the oscillations is given jointly by the Berry phase around the emergent Fermi surface and $\mathcal{X}$. When the band is narrow rather than exactly flat, the oscillation frequency crosses over, with a parameter-free profile, from the zero-field Fermi-surface area to the area of the $\mathcal{M}=0$ contour. We verify our analytical findings numerically by exactly diagonalizing a flattened Chern lattice model.

Quantum programming requires concepts and intuition that differ substantially from those used in traditional programming languages. Multiple quantum programming frameworks address these requirements through different abstractions and execution models, creating tradeoffs in programmability, portability, and performance. Because quantum software is less prevalent in the training data available to large language models, autonomous coding agents might be expected to face additional challenges when working with these frameworks. We present a case study in autonomous migration of the QED-C Application-Oriented Benchmarks from Qiskit to CUDA-Q. Opus, GPT, and Gemini models were each tasked with porting thirteen benchmark methods spanning foundational algorithms, amplitude estimation, Monte Carlo methods, variational optimization, linear-system solving, and factoring. In all cases, the models were able to generate satisfactory ports with little direct coding help and there was a drastic reduction in the time required compared to a human engineer. However, in all cases, the agents resorted to shortcuts when implementation challenges surfaced and multiple prompts were required to force appropriate coding directions. Assessment of the generated implementations showed that no single model consistently produced the best port across all benchmarks, indicating that multi-agent ensemble strategies could mitigate challenges associated with quantum software. The effort resulted in publicly available, near-complete CUDA-Q support in the QED-C benchmarks along with model-generated skills designed to improve agent performance when implementing CUDA-Q applications. When tested on an unseen benchmark, these skills reduced the required model turns by up to 85% and token usage by up to 96%. We conclude by distilling lessons from the case study.

Which pure states give uniform outcomes in several mutually unbiased measurements, and when can they form another measurement basis? We distinguish common unbiased states, their orthonormal completion, and further extension. For a displayed phase-deformed Fourier pair in dimension $2m$, a clock symmetry and skew antiunitary complete every prescribed common unbiased state to an explicit orthonormal basis. In dimension six, an amplitude-profile obstruction proves strong unextendibility of a given mutually unbiased triple when its original third-basis rays are invariant under the ternary clock. We give an exact nonproduct example and an application to a companion whose columns are localized at single Fourier frequencies. For every exact Hadamard matrix in a specified closed entrywise neighborhood of a fixed reference matrix, the coordinate and Hadamard bases have exactly 48 common unbiased rays but no third measurement basis, with a quantitative lower bound on the squared Gram defect of every six-column candidate. We also study auxiliary complex equations for common states, retaining algebraic multiplicities. For a flat unitary of order six with all $3\times3$ minors nonzero, the projective ray scheme is finite; each singular two-minor contributes two reduced isolated boundary points, and the affine coordinate algebra is finite-dimensional over $\mathbb{C}$. In every order, a critical-algebra identity preserves multiplicities; in dimension six it gives finiteness for a fixed phase fibre. Specialization over a discrete valuation ring controls complete reciprocal-orthogonal collections. A nonphysical Tao/Potts collection separates algebraic from physical solutions. These results do not determine the global maximum number of mutually unbiased bases or classify all triples.

In the present work we develop a model–independent method to reveal the underlying patterns of anisotropy and geometry in CMB temperature maps. This method, based on the rho-statistics and a process of mask–sweeping, unveils faint but persistent geometrical and periodic structures characterising the anisotropy of the CMB in a universe with 3-torus topology. We assess that the patterns detected over large ensembles of toroidal maps match geometrically to the layouts of expected circles-in-the-sky. The mask–sweeping method explores the whole CMB over 768 mask orientations. Thus we are able to re-verify the linear relation between L the size of the cubic torus and rho the standard deviation of the normalised variance of the CMB temperature gradient. This confers a finite size s of approximately 3 Hubble lengths to our Universe with toroidal topology. Furthermore, it is shown that the violation of statistical isotropy is so faint that this has almost no influence on the linear relation between rho and L. A topology like the cubic toroidal one imposes to our Universe periodic conditions (that can be seen as periodic boundary conditions) which constrain the global degree of inhomogeneity and a zero average curvature. We demonstrate here that rho is not only a statistical signature of the spatial finiteness and size of our Universe (may its topology be simply or multiply connected) but also outlines the patterns of anisotropy of the CMB with the help of the mask–sweeping process.

We study minimum-time navigation between two points in a known two-dimensional fluid flow for a vessel with fixed-magnitude slip velocity and controllable direction. The navigation policy is parameterized by a neural network and optimized using a genetic algorithm that evolves an ensemble of candidate strategies through Darwinian selection and parameter mutation, without crossover. The method recovers analytical and numerical optimal-control solutions in several benchmark cases, including two-dimensional turbulence, and outperforms Q-learning and one-step actor-critic method. We further show that the learned navigation strategies are robust to variations in the starting position and to unresolved small-scale turbulent fluctuations, provided that the characteristic velocity of these fluctuations remains small compared to the vessel's slip velocity. The proposed approach provides a compact representation of the navigation policy, is readily parallelizable, and eliminates the need for reward shaping, offering an efficient alternative to existing analytical and numerical methods for computing minimum-time trajectories in complex flows.

Jackiw-Teitelboim (JT) gravity with positive cosmological constant is a toy model to study the emergence of expanding de Sitter universes from a quantum state. Its density matrix is a superposition of double-trumpet amplitudes which are solutions of a Wheeler-DeWitt equation. We compare predictions of such mixed states for classical expanding universes with the corresponding predictions of no-boundary wavefunctions. Double-trumpet amplitudes consistent with holography, whose semiclassical limit matches the result from Schwarzian boundary fluctuations, lead to a flat probability distribution for the size of the universe, consistent with models of inflation. This is in contrast to the suppression of large universes predicted by no-boundary wave functions.

Step-index few-mode fibers (FMFs) provide a compact passive platform for high-speed photonic information processing by exploiting modal dispersion to map temporal inputs into spatiotemporal representations with short-term memory. In previous studies, we have demonstrated linear classification tasks with such FMFs, such as ultrafast multibit header recognition. Here, we extend the computational capability of such architectures towards solving nonlinear tasks by introducing an optical nonlinearity at the output of the dispersive medium. The FMF output is processed through parallel direct and nonlinear branches, with the latter implemented using a nonlinear semiconductor optical amplifier (SOA). We experimentally evaluate the architecture at 28.5 Gb/s on two delayed XOR and higher-order parity tasks. We show that combining the direct and SOA-transformed representations reduces classification errors and broadens the range of operating conditions supporting low-error classification. In addition, by exploiting multiple temporal samples within each bit period as independent classifier features, we increase the dimensionality of the photonic representation and improve the computational performance.

Large Eddy Simulation (LES) resolves only the large, energy-containing structures of turbulent flows while modeling the effects of unresolved subgrid scales. As a result, the ac- curacy of LES strongly depends on the subgrid-scale closure. This work investigates the online calibration of a spectral viscosity closure for forced two-dimensional turbulence using ensemble Kalman Inversion (EKI). The closure consists of a wavenumber-dependent viscosity function, which is parameterized by a 16-dimensional vector. An ensemble of potential parameter vec- tors is passed through coarse resolution LES simulations, and the resulting kinetic energy spec- tra are compared with a DNS reference spectrum up to the LES-resolved wavenumber range. Then, ensemble statistics iteratively update the spectral viscosity parameters in order to reduce the model-data discrepancy. The convergence of the spectral mismatch is used to assess the accuracy of the calibrated closure by comparing LES with the filtered DNS data in spectral space. The results demonstrate the potential of EKI-based online calibration as a derivative- free framework for constructing data-driven turbulence closures with multiple parameters.

We present a post-processing pipeline that predicts CO line luminosities, molecular hydrogen masses, and CO abundances for galaxies in the Feedback in Realistic Environments (FIRE-2) simulations, as a first step toward CO response functions for line intensity mapping mocks. SKIRT gives the radiation field reaching each gas cell. CLOUDY, tabulated on a five-parameter grid (metallicity, density, turbulent velocity, radiation field, cloud scale) and interpolated, gives $L'_{\rm CO(1-0)}$ and the $\rm H_2$ and CO masses. In this first of three papers we validate the pipeline against observations. For Milky Way (MW) molecular clouds, the predicted $L'_{\rm CO(1-0)}$ rises sharply at $A_V\sim1$ and matches the observed values above that column. For a uniform $3\,Z_\odot$ the summed luminosity is within 35% of the observed total. A factor of three increase in metallicity changes the total $L'_{\rm CO(1-0)}$ by an order of magnitude. Along MW sight lines, the predicted $N_{\rm CO}/N_{\rm H_2}$ reproduces the observed upturn within 0.3 dex in $N_{\rm H_2}$ but falls below the data at higher columns. At $z=0$ our molecular gas masses agree with xCOLD GASS for ${\rm SFR}\gtrsim0.3\,M_\odot\,yr^{-1}$ and are converged across our simulation resolutions. At $z\gtrsim1$ observed gas masses exceed ours at fixed stellar mass, though selection effects and conversion-factor assumptions limit this comparison. CO luminosities are compared with extragalactic observations in Paper II. Capturing the metallicity-dependent dust shielding of CO from photodissociation is essential for modeling CO emission in low-mass and high-redshift galaxies.

We report a direct measurement of the 21-cm signal from neutral hydrogen (HI) via the intensity mapping technique at redshift $z = 0.42$ through its power spectrum at cosmological scales $0.095\,h\,{\rm Mpc}^{-1}<k<0.245\,h\,{\rm Mpc}^{-1}$. We analyse intensity maps observed in the frequency range $971.2\,{\rm MHz}<\nu<1023.6\,{\rm MHz}$ during the 2021 observational campaign of the MeerKAT Large Area Synoptic Survey (MeerKLASS) spanning an area of approximately $236\,{\rm deg}^2$. Capitalising on the depth of this survey, we divide the data set into multiple observationally independent subsets. After performing blind contaminant separation independently on each subset, we exploit internal cross-correlations to suppress the instrumental noise plateau and isolate the cosmological signal from residual systematics. Although the subsets' auto-spectra show the presence of excess power that we deem linked to still unresolved systematics, the cross-spectra measurements satisfactorily pass a battery of null and robustness tests. We introduce a multi-subset formalism to infer the amplitude of the HI auto-spectrum from our internal cross-spectra measurements while marginalising the potential signal loss that occurs during data processing, reaching a detection with signal-to-noise ratio of 6.2. We constrain the clustering amplitude of the signal in terms of the HI cosmic abundance and its linear bias, measuring $10^3\,\Omega_{\rm HI} b_{\rm HI}=1.271^{+1.080}_{-0.598}$. This direct measurement marks a milestone in demonstrating the effectiveness of the HI intensity mapping technique for future surveys with the SKA Observatory, of which MeerKAT is a precursor.

We investigate turbulent dynamics from large to sub-proton scales, the kinetic wave activity and resulting proton heating in three-dimensional hybrid-kinetic simulations of freely decaying balanced and imbalanced Alfvénic turbulence. We considered initial fluctuations with same amplitude and spectral shape but different degrees of correlation between velocity and magnetic field perturbations (i.e., normalized cross-helicity), which define the corresponding turbulent regimes. We found that our balanced turbulence simulation exhibits stronger energy dissipation and more efficient proton heating compared to the imbalanced case. On the large fluid scales, the two simulations undergo distinct nonlinear dynamic and energy cascades, leading to different spectral properties once turbulence is well developed. At sub-proton scales, fluctuations are predominantly oblique with right-hand polarization, a property common to both regimes. In contrast, only the imbalanced simulation displays substantial wave power parallel to the guide magnetic field, with clear signatures of ion-cyclotron waves. These waves are excited in regions with strong temperature anisotropy that arise during the initial nonlinear steepening of Alfvénic fluctuations, a process that simultaneously generates parallel-propagating proton beams. Furthermore, non-Maxwellian features emerge in velocity space, indicating different heating mechanisms in balanced and imbalanced regimes and providing evidence for the role of kinetic instabilities in regulating the turbulent dynamics. Overall, these results show that cross-helicity is a key parameter controlling the large-scale evolution, kinetic activity, and energy dissipation in collisionless turbulent plasmas.

Geodynamic simulations involve solving the Stokes equations, where boundary conditions play a crucial role in governing internal dynamics and surface topography evolution. A common approach is to model the interface in contact with air as a free surface. This interface is stress-free and deforms in response to internal force balances, which is essential for understanding processes ranging from rifted margins to subduction. However, a free surface can introduce instabilities and oscillations in the numerical solution, commonly known as the "drunken sailor" instability, forcing the use of small time steps. Geodynamic models therefore often employ stabilization terms based on the gravitational effect of free-surface motion. To date, these terms have been derived primarily on a heuristic basis. Here, we apply variational calculus to the energy-minimization formulation of incompressible Stokes flow. Using the Gateaux derivative, we derive the complete weak form for a deforming free-surface domain and show that the stabilization terms arise naturally from the free-surface assumption. We consider the linear-viscosity case and identify two boundary-integral contributions: one associated with internal viscous deformation and the other with gravitational body force. Two benchmark cases demonstrate the consistency of the derived formulation with previously proposed stabilization approaches. Our results provide a mathematical foundation for the commonly used gravity stabilization and identify an additional viscous boundary contribution.

Machine learning interatomic potentials (MLIPs) are transforming atomistic simulations by accessing unprecedented length and time scales. While pretrained equivariant graph neural networks achieve robust zero-shot performance for near-equilibrium properties across broad chemical spaces, their translation to complex materials dynamics remains fundamentally challenged by out-of-distribution reactive states, representation biases, and computational scaling limits. In this Review, we examine how physics-driven specialization extends the applicability of MLIPs to complex dynamical systems. We systematically evaluate the structural trade-offs in MLIP design: the undersampling of highly strained configurations, the prohibitive computational overhead of high-order message-passing architectures, and the necessity of nonlocal interactions for open and field-coupled systems. Through four demanding application contexts - electrified interfaces, compositionally fluctuating open systems, multiphase evolution, and large-scale fracture - we establish a framework for observable-specific validation. Highlighting the complementary roles of universal foundation models and task-specific potentials, we emphasize that targeted adaptations must be rigorously benchmarked against intended observables. We conclude with a roadmap for developing physically consistent, hardware-aware force fields that seamlessly connect electronic-structure accuracy to macroscopic materials phenomena.

Quantum machine learning, and quantum neural networks (QNNs) in particular, are advancing fields with growing potential. Although systematic comparisons of QNN configurations have been explored primarily for classification tasks, comparatively little attention has been given to regression problems, particularly financial time series forecasting. This study presents a large-scale systematic comparative evaluation of QNN component configurations for financial time series forecasting, using the GBP/USD spot exchange rate as a case study. A grid search across encoding methods, ansatz designs, qubit counts, layer depths, and cost functions yields 1,368 distinct model configurations, each evaluated in terms of prediction accuracy, computational cost, and convergence behaviour. The results reveal unique insights into how the choice of methods influences performance, such as that gate selection and arrangement are more critical to model success than raw parameter count, and that entanglement is a system-level property of the full circuit rather than solely at the ansatz level. The best-performing QNN configuration achieves an $R^2$ score of 0.985, outperforming a classical BiLSTM baseline. Additionally, the impact of real quantum hardware noise is assessed through execution on the IQM Emerald device, revealing that gate errors and decoherence represent a significant barrier to practical deployment, with gate selection and circuit depth identified as key determinants of hardware noise resilience. Overall, the findings provide practical architectural guidance for QNN design and establish a baseline characterisation of QNN noise sensitivity on near-term quantum devices.

Ultrafast science is rapidly evolving from a collection of specialized laser experiments into a facility-scale, data-intensive research endeavour. High-repetition-rate femtosecond and attosecond sources, synchronized multidimensional diagnostics, and increasingly nonlinear laser-matter interactions now generate heterogeneous data streams whose volume, speed, and complexity often exceed the capacity of conventional manual workflows. This chapter examines how machine learning can become an enabling layer for next-generation ultrafast facilities, with emphasis on the Extreme Light Infrastructure (ELI) ecosystem. We discuss the scientific drivers for artificial-intelligence-assisted experimentation, propose a facility-scale digital architecture spanning acquisition, ingestion, storage, analytics, machine learning, adaptive feedback, and user interfaces, and review representative examples from ELI ALPS, ELI Beamlines, and ELI-NP. The central thesis is that machine learning should not be treated merely as an offline data-analysis tool; rather, when constrained by physical insight and embedded in robust data infrastructure, it can support real-time diagnostics, surrogate modeling, anomaly detection, experimental optimization, and progressively autonomous discovery workflows.

Chirality-Induced Spin Selectivity (CISS) refers to structural chirality governing spin transport in chiral systems, with experimental signatures reported in both transport experiments and photoemission. Beyond influencing transport, recent experiments indicate that adsorbed chiral molecules can influence the magnetic state of adjacent ferromagnetic layers, suggesting the existence of chirality-induced effects whose origin and quantitative characterization remain lacking. Here, we investigate chirality-induced long-range magnetic interactions in a hybrid molecular/ferromagnetic multilayer beyond the conventional direct transport coupling. We demonstrate the long-range nature by decoupling the adsorbed chiral molecules from the magnet using an insulating spacer. We show that self-assembled monolayers of chiral polypeptides with a helical structure can act on a ferromagnetic multilayer via an enantiomer-dependent effective magnetic field that may be mediated by chiral phonons through the insulating layer. Using Magneto-Optical Kerr Effect (MOKE) microscopy with on-chip reference regions free of molecules, we directly quantify this field by measuring precisely the reproducible shifts of the local hysteresis loops. The extracted effective field reverses sign for opposite enantiomers and correlates with the enantiomeric excess. These results provide a direct and quantitative link between molecular chirality and magnetic response in hybrid heterostructures, establishing an experimentally accessible benchmark for a CISS-related effect.

We report the discovery of a new radio pulsar PSR J0103$-$72 in the Small Magellanic Cloud (SMC) through a reanalysis of Parkes/Murriyang archival observations targeting supernova remnants and pulsar-wind nebulae in the SMC. Previously two pulsars were discovered in these observations, and PSR J0103$-$72 is the third which we independently detected in two observations of its target field. It has a spin period of 1.327 sec and a dispersion measure (DM) of $272\pm13~\mathrm{pc\,cm^{-3}}$, making it the slowest known radio pulsar in the SMC with the second-highest DM. We did not identify any plausible supernova remnant or pulsar-wind nebula associated with the pulsar. Subtracting the predicted Galactic foreground leaves a DM contribution of $\sim 230$–$242~\mathrm{pc\,cm^{-3}}$, which may suggest a long path through the ionized medium of the SMC, enhanced ionized gas along the sightline, or both. Although these data had previously been searched using both FFT and fast folding algorithms, our FFT-based reprocessing found PSR J0103$-$72 through search techniques which have previously been highly effective in discovering a large number of pulsars in other surveys. This discovery shows that candidates with weak Fourier-domain significance can reveal faint, narrow-duty cycle pulsars missed in earlier extragalactic pulsar searches.

Standard convolutional neural networks for turbulence super-resolution are restricted to uniform structured grids, preventing their direct application to the non-uniform and non-orthogonal meshes commonly used in wall-bounded flows and complex geometries. We introduce a finite-volume convolution unit (FVCU) that removes this restriction. The key idea is to construct convolutional layers using finite-volume operators that are applicable to non-uniform and unstructured meshes. To this end, we use an exact representation of a 3D convolution as a linear combination of successive axis-wise convolutions. We adopt axis-wise gradient, averaging, and identity operators, as they can be evaluated on arbitrary polyhedral cells using finite-volume discretization. The resulting FVCU is well-defined on general meshes, serving as a drop-in replacement for conventional convolution layers. The FVCU is embedded in a super-resolution neural network and trained to reconstruct 3D velocity fields from $4\times$ coarsened low-resolution fields in turbulent channel flow on a non-uniform mesh, and in circular-pipe flow on a non-orthogonal O-grid mesh. Inspired by the role of the Van Driest damping function in near-wall subgrid-scale modelling in large-eddy simulations, this function is supplied as a conditional input to encode wall proximity. An ablation study confirms that the network interprets this input in a physically consistent manner. In both configurations, the model accurately reconstructs instantaneous velocity fields, Reynolds-stresses and their anisotropy, and energy spectra beyond the Nyquist limit of the low-resolution mesh. On an elliptic pipe unseen during training, the model reproduces the main flow features and local statistics, with a modest overprediction of small-scale energy. These results demonstrate a framework for extending CNN-based super-resolution to general finite-volume grids.

High harmonic generation (HHG) has emerged as a powerful probe of ultrafast electron dynamics in solids. However, in strongly correlated materials, where electronic excitations arise from many-body interactions rather than single-particle band dispersion, the field remains in its infancy. Here, we report HHG up to the 23$^{rd}$ order in the prototypical Mott insulator La$_2$CuO$_4$. We observe a robust 4 eV cutoff that is independent of driving field strength, revealing an intrinsic energy scale defined by the Hubbard bands. Using time-resolved high harmonic spectroscopy, we disentangle the distinct contributions of carrier creation, intraband acceleration, and interband recombination to the nonequilibrium radiation spectrum. We demonstrate that photo-doping triggers a fundamental reorganization of the many-body manifolds above a critical excitation density, a process that renormalizes the band curvature and suppresses nonlinear intraband currents, thereby directly imprinting the signature of many-body band reconstruction onto the harmonic emission. These results establish HHG as a sensitive probe of subtle correlation-driven electronic reconstructions and highlight that many-body interactions enable extreme nonlinear optical responses that are highly tunable in nonequilibrium states.

Predicting the structure and response of inhomogeneous polymer electrolytes requires a description of ion correlations that retains molecular-scale accuracy while remaining transferable across spatial scales and geometries. We develop a neural density functional for electrolytes that preserves spatial symmetries, thermodynamic integrability and the Noether identities, with perfect screening recovered in stable, noncritical bulk states. Its nonlinear density dependence captures the concentration-dependent correlations missed by a pair closure, including a crossover from enhanced to suppressed long-wavelength number fluctuations at strong coupling. The functional describes density profiles at an untrained salt concentration and predicts bulk structure factors and the long-wavelength number response. Trained solely on planar density and internal-force profiles from molecular dynamics, the functional predicts ionic structure in larger domains and in two-dimensional external fields. On the same ion data, it is more accurate than three other neural density-functional architectures and keeps its accuracy with a quarter of the training runs, where the errors of the best alternative grow by about two thirds. The spatial transferability provides a necessary foundation for connecting molecular correlations to continuum predictions at larger scales.

Hyperon polarization in relativistic heavy-ion collisions sensitively probes thermal vorticity and the operator structure of local equilibrium. We investigate an isotropic generalized uncertainty principle (GUP) via a spin-independent scalar deformation of the one-particle phase-space density of states in the local rest frame (LRF). We employ the Belinfante-Rosenfeld local-equilibrium operator, the standard on-shell dispersion relation, and the Pauli-Lubanski/axial-Wigner representation of the spin-$1/2$ observable, retaining terms linear in the GUP coefficient and in hydrodynamic gradients. The same scalar GUP factor appears in the scalar and axial phase-space contributions and cancels from the local mean-spin ratio at fixed spacetime point and momentum. Thus, the leading GUP contribution to an experimentally relevant polarization observable arises through the Cooper-Frye freeze-out integrals and can be expressed as a covariance between the LRF momentum invariant and the local spin kernel. This cancellation is a consequence of the scalar density-of-states modification within a fixed pseudogauge and does not imply pseudogauge independence of the polarization observable. For $\Lambda$ hyperons, a Maxwell-Boltzmann thermal estimate shows that the dependence on freeze-out temperature and fluid-dynamical correlations is substantially larger than the Fermi-Dirac correction, which remains negligible over the temperature range considered. Present LHC precision corresponds only to a formal linear sensitivity of order $10$-$30~\mathrm{GeV}^{-2}$. Since this interval lies beyond the domain in which the linear GUP expansion is reliable, no exclusion limit is inferred. Differential measurements of hyperon polarization as functions of momentum, rapidity, collision centrality, and azimuthal harmonic structure provide the most direct experimental avenue for testing the predicted covariance dependence.