Papers

Water dissociation on metal oxide surfaces is a key elementary step in heterogeneous catalysis, photocatalysis, and radiation chemistry, yet its mechanistic details on rare-earth (RE) sesquioxides remain poorly understood. Here, we investigate water dissociation on the (110) surfaces of three cubic bixbyite oxides, Sc$_2$O$_3$, Y$_2$O$_3$, and Lu$_2$O$_3$, using molecular dynamics combining ab initio calculations with on-the-fly machine-learning force field acceleration. By sampling 25 independent trajectories per material, we obtain an unbiased picture of the reaction landscape inaccessible to conventional static calculations. Two distinct dissociation pathways are identified: a conventional proximal mechanism with a small but finite barrier of $\sim$0.1 eV, and a previously unreported distal mechanism that is effectively barrierless and energetically preferred at both the adsorption and dissociation stages. The low barriers are consistent with the periodic array of inherently undercoordinated RE$^{3+}$ sites in the bixbyite lattice, suggesting that ordered intrinsic coordination defects play a role analogous to stochastic oxygen vacancies in conventional oxides.

It is demonstrated experimentally that the effective separation of bi-dispersed microspheres (dust particles) in the underdamped and strongly-coupled regime is realized using a designed dusty plasma ratchet. Experimental findings reveal that these dust particles can undergo directional transport at varying speeds, even moving in opposite directions depending on the discharge conditions, enabling successful particle separation. Numerical simulations of the plasma environment surrounding the dust particles are performed using fluid simulations of the capacitively coupled discharge of Argon. The simulation results indicate that the bi-dispersed dust particles are suspended at different balance heights within the plasma sheath and experience distinct ratchet potentials that govern their directional transport, resulting in varied flow velocities. The discovery of height-dependent transport of dust particles here provides insights of transport fundamental of underdamped strongly-coupled particles in dusty plasma ratchets.

A single dust particle confined in an asymmetric ratchet potential is periodically driven by two oppositely directed laser beams, forming an underdamped dusty plasma rocking ratchet. We experimentally investigate the transport dynamics of the particle under varying driving amplitudes and frequencies. Depending on the driving conditions, the particle exhibits positive, zero, or negative net currents, and current reversal is observed when the driving parameters cross critical thresholds. To interpret these transport behaviors, we develop a simplified model based on the competition between the driving force and the ratchet confinement. The model reveals that directional transport is governed by two requirements: the driving force must exceed the depinning threshold, and the duration of a driving semicycle must be longer than the uphill escape time from a ratchet well. The resulting dynamic phase diagram quantitatively reproduces the experimentally observed transport regimes and current reversals. These results demonstrate dusty plasma as a versatile platform for investigating nonequilibrium transport phenomena of underdamped particles in rocking ratchets.

Strongly enforced Dirichlet boundary conditions require highly refined near-wall meshes to resolve steep velocity and thermal gradients. This introduces high computational costs, especially for practical flow simulations. Weakly enforced boundary conditions alleviate this burden by acting as a variationally consistent near-wall model. By allowing a controlled slip at the solid wall, weak enforcement recovers accurate flow quantities on coarse boundary-layer meshes across both incompressible and compressible regimes. Furthermore, weak boundary conditions serve as the fundamental enabling technology for immersogeometric analysis. Because the weak operator evaluates boundary integrals independently of the background mesh, high-fidelity flow analysis can be performed directly on complex geometries without fitting a mesh to the surface. This capability has facilitated direct geometry-to-analysis workflows for boundary-representation CAD models, raw point clouds, photogrammetric reconstructions, and segmented medical images. This review examines the unified mathematical development of the weak boundary condition framework from scalar advection-diffusion equations to the full Navier-Stokes equations, illustrating its versatility and robustness across incompressible and compressible flows, whether in traditional boundary-fitted, sliding-interface, or advanced immersogeometric applications.

We propose a symmetry-preserving Boson-informed neural network (BINN) for the variational Monte Carlo computation of interacting-boson ground states in continuous space. The framework explicitly separates the two dominant physical scales of the system. At the microscopic interaction scale, a cell Jastrow factor absorbs the leading near-contact stiffness, while at the macroscopic scale, a potential-aware envelope encodes the external confinement, leaving the neural network to capture a smoother many-body residual. Across both two- and three-dimensional tests, the BINN approaches the Hartree and Gross-Pitaevskii limits with an empirical $\mathcal{O}(N^{-1})$ finite-size trend in both energy and density. The energies agree with diffusion Monte Carlo to within $0.06\%$ for the particle number tested up to $350$ in the three-dimensional shrinking-range case. Applications are made under various trapping scenarios, justifying robust performance of BINN.

The aim of this work is to predict the opacity of plasmas under stellar conditions. We focus on iron and nickel, as these elements have been extensively investigated both theoretically and experimentally. In certain regimes, notably under non-local thermodynamic equilibrium, calculating the spectral opacity can be computationally demanding. To mitigate this difficulty, we employ deep learning models to predict accurate opacities while achieving a substantial reduction in computational cost. Specifically, we develop and train a hybrid model that combines a convolutional neural network with a multilayer perceptron. The proposed approach provides both the spectral opacity over the 0–10,000 eV energy range, and the corresponding mean opacities for large sets of temperatures and mass densities, in close agreement with standard computational methods. The agreement is very satisfactory, except in the L- and M-shell energy ranges for nickel at high temperatures, where a very large number of emerging transitions leads to substantial discrepancies.

Appeals to typicality are common in physics, but it is often unclear what it means for a physical state to be typical relative to a probability measure, and correspondingly unclear what a law that appeals to typicality asserts. Here we consider how one might characterize physical typicality using ideas from the theory of algorithmic randomness. As a concrete example, we show how taking a physical state to be typical relative to a computable measure when it is Martin-Löf random allows one to formulate the distribution postulate in Bohmian mechanics as a statistical constraining law of the theory. Using a toy model, we show how this constraint guarantees the standard Born statistics for computable experimental protocols. Algorithmic Bohmian mechanics (aBM) thus illustrates how algorithmic randomness may be used to provide precise content to a statistical law.

The automatic derivation turns a declared system of conservation laws into a lattice Boltzmann scheme, giving each conserved physical quantity a set of q populations whose linear equilibrium embeds the physical flux in their first moment. When the flux depends on gradients of the conserved state, those gradients are supplied by tracking them as additional transported fields. Since lattice Boltzmann is commonly memory-bound, these additional degrees of freedom reduce the achievable throughput. To reclaim it, we reconstruct the gradients from the moment structure of the equilibrium instead of transporting them. To leading order, the first moment of a conserved quantity's non-equilibrium part carries its gradient. Because the reconstructed flux enters its own equilibrium reference, that moment is the image of the gradient under a linear operator built from the diffusive-flux Jacobian. The reconstruction is that operator's algebraic inverse, generic across gradient-form constitutive closures and generated automatically from each declared flux. The resulting scheme carries the conserved quantities alone, reconstructing the required gradients from the populations and forming the fluxes locally. It converges at second order in double precision across advection-diffusion-reaction, Allen-Cahn, Navier-Stokes, resistive magnetohydrodynamics and homogenized compressible Navier-Stokes-Fourier systems, matching the accuracy of gradient tracking at equal resolution. On an NVIDIA RTX A5000 it is up to 3.7 times faster in single precision, the memory-bound kernels reaching up to 97% of peak memory bandwidth.

Optical tweezers are a versatile tool in the domain of cytology, enabling trapping and manipulation of individual cells. However, the incidence of laser light causes photodamage to biological matter, even when operating at low optical powers and over short periods of time. Here, we demonstrate stable trapping of single living Saccharomyces cerevisiae yeast cells in vitro using a dark optical trap operating in the repulsive regime of light-matter interactions. In contrast to standard tweezers, our dark optical trap confines cells for hours with negligible disruption to their morphology and reproduction cycle, opening up new possibilities for long duration experiments with living organisms such as the observation of cell reproduction under laser trapping.

A computation-based study is presented that utilizes the finite-difference time-domain (FDTD) method to perform optical simulations. These simulations were used to observe the effects of core-shell nanostructures with triangular cross-sections embedded inside the light absorbing layer of thin-film solar cells (TFSCs), for improving the optoelectronic performance. Despite extensive research, the relationship between the geometric parameters of light trapping nanostructures and TFSC performance is not yet fully elucidated. To assist in finding the optimal set of parameters, optimization algorithms are used to find a set of parameters that provide maximal short-circuit current density ($J_{SC}$). Three separate algorithms were applied, and the results were scrutinized in-depth with a sensitivity analysis, observing the effects of small changes in the optimal parameters. The optimal parameters of the nanostructure produced $J_{SC}$ values ranging from 28.90 to 35.39 mA/cm$^2$ compared to 13.69 mA/cm$^2$ for a reference TFSC with no nanostructure present. From the results of this study, it can be inferred that optimized periodic grating structures can provide substantial improvement in performance of TFSCs.

Delocalisation error has been argued to be the greatest outstanding challenge in density-functional theory (DFT). One of the most promising routes to minimise this error is development of local hybrid functionals, in which the fraction of exact-exchange mixing is position dependent. However, existing local hybrids capable of good thermochemical accuracy are often highly empirical, and tend to have complicated functional forms that involve some combination of range separation, calibration functions, power-series expansions, or even neural networks. In this work, we explore the limits of what can be achieved with a minimally empirical local hybrid functional form that avoids such complexities. The “LHnz” functional is proposed, which uses dispersionless exchange and a local mixing fraction dependent on the correlation length and effective exchange–correlation hole normalisations. With only three empirical parameters, LHnz is shown to outperform all existing global hybrid functionals for the GMTKN55 molecular-thermochemistry benchmark with no large outliers.

We develop a theory that determines the radial profile of the mean velocity in a coherent geostrophic vortex forming in a turbulent rapidly rotating fluid. Following the conditions of our experiments, we assume that the flow in the vortex is sustained by the absorption of short-wavelength inertial waves arriving from the vortex periphery. The nonlinear interaction between waves is assumed negligible in the theory. Comparison with experimental data supports the validity of the developed model.

Deep-space exploration beyond the solar system requires eliminating chemical propellant mass penalties to achieve relativistic flight velocities (0.166c at 180 s, reaching the mission target of 0.20c at 227 s). This study presents a 3D multi-physics numerical framework for a 4.0-meter circular lightsail propelled by a 100 GW ground laser array, coupling 3D Maxwell FDTD wave optics, non-linear membrane mechanics, and Stefan-Boltzmann thermal radiation in ANSYS Mechanical APDL and Ansys Lumerical. A four-level grid convergence study establishes numerical independence with an ASME GCI_21 of 0.13%, resolving peak membrane stresses of 530.88 MPa with a 3.77x safety factor against stoichiometric Si3N4 tensile failure. With optical absorption constrained to 10 ppm (A = 1.0 x 10^-5), the steady-state core temperature stabilizes at 923.02 K (0.44% deviation from radiation theory), maintaining a +1,247 K margin below sublimation, while fundamental drumhead modal resonance (7.92 Hz) provides a 7.92x safety buffer against laser jitter. The electrodynamic radiation pressure formulation is cross-verified against published flight telemetry from JAXA IKAROS and NASA LightSail 2 within 0.12% and 2.13%, confirming classical momentum transfer modeling across solar and beamed propulsion regimes.

The Cr$_2$O$_3$ solid target for low-energy $^{16}$O+$^{16}$O fusion experiments was fabricated using filtered cathodic vacuum arc (FCVA) deposition. Its composition, oxygen areal density and impurity content were characterized by elastic backscattering spectrometry (EBS), and the impurity-induced background contributions were investigated by in-beam $\gamma$-ray spectroscopy. EBS results indicate that the Cr$_2$O$_3$ film exhibits good stoichiometry and uniformity, with $^{16}$O areal densities ranging from $(3.24-3.25)\times10^{17}$ atoms/cm$^2$. The EBS analysis reveals a carbon atomic fraction of approximately 1.25-1.29% in the Cr$_2$O$_3$ layer, while a large amount of carbon impurities are also identified on the surface of Cr substrate. In-beam $\gamma$-ray spectra reveal prominent transitions associated with $^{27}$Al and $^{24}$Mg at 844, 1015, and 1369 keV, mainly originating from $^{12}$C+$^{16}$O fusion reactions induced by carbon impurities under $^{16}$O irradiation. Meanwhile, characteristic $\gamma$-rays emissions from evaporation channels of the $^{16}$O+$^{16}$O reaction, including $^{31}$S, $^{31}$P, and $^{28}$Si, were also observed and can be used to extract the $^{16}$O+$^{16}$O fusion cross sections. This work provides an experimental basis for the development of high-purity oxide targets and the optimization of target configurations for future low-background $^{16}$O+$^{16}$O fusion cross section measurements.

Low-frequency cavity breathing oscillations can strongly affect flame stabilization and operability in cavity-stabilized supersonic combustors, yet their hydrodynamic origin and suppression remain insufficiently understood in opposed twin-cavity configurations. We investigate the breathing dynamics of an opposed twin-cavity supersonic combustor under low- and high-enthalpy conditions using synchronized high-speed Schlieren imaging, wall-pressure measurements, numerical simulations, nonlinear phase-space reconstruction, Hilbert-transform analysis, and reduced-order modelling. Under low-enthalpy conditions, a self-sustained breathing mode is observed at a dominant frequency of approximately 156 Hz, with large phase differences between cavity-floor and ramp pressures. Measurements and simulations show that periodic mass exchange between the cavity and freestream drives cyclic cavity depressurization and repressurization, establishing a pressure-shear-layer feedback loop that sustains the oscillation. A two-pressure-state reduced-order model, coupled with a shear-layer displacement coordinate, reproduces the measured frequency, pressure phase relationship, and stability characteristics, demonstrating that both bulk-cavity and reattachment/shock-foot pressure states are required to capture the dynamics. Upstream nitrogen injection produces only transient attenuation, with the oscillation recovering as the injected gas convects downstream. In contrast, ethylene injection followed by ignition permanently suppresses the oscillation, with the nonlinear attractor collapsing to a stable equilibrium. Heat release reorganizes the cavity pressure field and alters density, compressibility, and pressure-response timescales, thereby weakening the feedback mechanism responsible for sustaining the breathing mode.

Sloshing is an inevitable phenomenon in an ocean-going vessel that can have adverse effects. In this work, the mitigation of sloshing is investigated using multiple thin porous baffles of various configurations in a partially filled swaying tank. The boundary value problem is solved within the framework of a linearized potential flow theory using the scaled boundary finite element method (SBFEM). The flow through the thin porous baffles is assumed to follow Darcy's law. The computational domain is divided into a minimum number of subdomains due to the presence of porous baffles and to ensure star convexity. Higher-order polynomials are used along each subdomain edge to represent the unknown field, i.e., velocity potential. The developed numerical model is validated with the known results in the literature. Subsequently, various results, such as the amplification factor and the forces of the tank wall, are presented and discussed for the effect of configuration, porosity, slosh tank width, depth of baffle submergence and the space between adjacent baffles. From the parametric study, it is observed that top-mounted baffles enhance sloshing suppression by $50\%$ compared to bottom-mounted vertical baffles, considering all sloshing modes. Assessing the overall effectiveness, top-mounted convex baffle configuration emerges as the most efficient configuration for sloshing suppression, achieving a well-balanced reduction across all modes.

Incremental contact laws specify the normal contact force through a differential relation, possibly carrying an internal state, driven by the indentation and its rate. In some, the force is extinguished at a nonzero indentation, whether by plastic deformation or by an elastic aftereffect, so that a residual deformation remains at separation. Such laws sit uneasily within rigid body dynamics, which admits no deformation. The tension is tolerable when the indentation is small relative to the bodies, so that it may be carried constitutively rather than geometrically. Even then, the contact law alone does not determine the interaction of the bodies. Because force and indentation no longer vanish together, conditions for the commencement and termination of contact must be supplied separately. So must the fate of the deformation and internal state at separation, neither of which the equations of motion contain. This article formulates the repeated direct collinear impact of two convex bodies under external forces as a hybrid dynamical system. The contact interface is modeled as a massless element carrying the contact law and its state, coupled to the bodies through relative velocity and an interaction force dictated by the contact state. Consequently, all switching and resets are confined to the interface, leaving the geometry and the equations of motion of the bodies unaltered. The principal analytical properties of the resulting formulations are established, among them passivity and completeness; the branching of solutions at the onset and termination of contact is also examined. The framework is demonstrated through numerical simulations.

Reconstructing physical fields from sparse observations is central to system identification, forecasting, and control, yet sparse measurements generally underdetermine the full field. This makes reconstruction an ill-posed inverse problem rather than simple interpolation. Although many deterministic and generative methods have been developed, there is still no clear consensus on when a single point estimate is sufficient and when a distribution of plausible reconstructions is more useful. We conduct a fair comparison of a deterministic U-Net, conditional diffusion, and prior-guided diffusion under matched experimental settings, including 2D Poisson equation, 2D Navier-Stokes flow, and 1D Kuramoto-Sivashinsky dynamics. Through this comparison, we make three observations. First, accuracy is field- and regime-dependent, with no systematic advantage for diffusion under higher complexity or sparser observations. Second, ensemble means improve phase-aligned accuracy, whereas individual samples better preserve variability and can retain high-wavenumber power in selected regimes. Third, conditional diffusion provides more reliable uncertainty estimates at lower cost, while prior-guided diffusion is more robust to mask-distribution shifts but requires substantially higher inference cost and guidance tuning. These results clarify when generative reconstruction is useful and provide guidance for improving uncertainty estimation, fine-scale sample fidelity, robustness, and computational efficiency in sparse field reconstruction.

I recently showed how objective Everettian probability can be defined over the records borne by an observer's later continuations, without unique actualization or additional premeasurement subjects \citep{Baxter2026}. Here I extend that model to all decoherent physical record states, including records that support no such continuation. The observer-indexed model is then a restriction of an exhaustive physical-record model.

Topological materials provide a transformative arena for light-driven control of electronic motion; yet, direct manipulation of electron dynamics on sub-cycle timescale remains a significant challenge. We demonstrate that the strong-field-driven high-harmonic generation of a Weyl semimetal can be controlled and manipulated through bicircular pump-probe driving fields. While a lone probe pulse generates exclusively odd-order harmonics, the addition of a pump pulse triggers a series of sidebands arising from nonlinear frequency mixing of pump and probe photons. Our results reveal that the sideband intensities are highly sensitive to the relative helicity of the pulses and the orientation of the polarization plane. This sensitivity stems from the chiral nature of the Weyl nodes, which couples efficiently to the light's helicity only when the node-separation axis is perpendicular to the polarization plane. Furthermore, the significant suppression of sideband intensity with increasing pump-probe delay identifies these features as a potential clock for electron-hole decoherence. These findings establish frequency-mixed high-harmonic generation as a sensitive probe of chiral quantum dynamics and suggest a robust framework for manipulating topological currents via structured light, with implications for lightwave electronics and ultrafast quantum information processing.

AC-coupled Low Gain Avalanche Diode (AC-LGAD), evolved from the standard LGAD technology, are silicon detectors characterized by exceptional temporal and spatial resolutions. This work proposes a novel sensor named AC-coupled Cross Strip LGAD (CS-LGAD) structure with dual-layered overlapping strip electrodes. It can achieve two-dimensional particle position measurement with low readout density while also inheriting the high temporal resolution of the LGAD. The sensor structure, doping and current-voltage (I-V) characteristics of the sensor were investigated through TCAD simulations. Additionally, Monte Carlo simulations were employed to simulate the signal response of CS-LGAD to minimum ionizing particles (MIPs). The response of MIPs hitting different positions was simulated, and the reconstruction of particle coordinates (x and y) was achieved based on the sharing of charges on the dual-layer electrodes. The spatial resolution might reach 3.6% and 5.5% of the pitch size in the x and y directions, respectively, according to the simulation. The proposed CS-LGAD is a promising 4D detector with low mass and low readout density.

We present a construction-constrained Bernstein framework for joint pitch-width synthesis of planar spiral inductors. A positive cubic Bernstein profile is integrated to prescribe a monotonic radial trajectory, while an independent Bernstein profile controls the conductor width. The nonnegativity, partition-of-unity, and convex-hull properties of the Bernstein basis convert endpoint, winding-direction, and width requirements into algebraic constraints. Embedded in differential evolution (DE), these constraints reject inadmissible candidates before full-wave evaluation, focusing expensive solver calls on geometries that satisfy the construction rules. The method is demonstrated on a two-turn mmWave inductor implemented in the redistribution layer (RDL) of fan-out wafer-level packaging (FoWLP), with the quality factor selected as the optimization objective. Searching the constrained Bernstein design space with DE yields a simulated Q of 34.85 at 30 GHz, compared with 29.66 for a uniform Archimedean baseline under the same stack, footprint, and ports. This corresponds to a 17.5% improvement while reducing planar copper area by 1.53%. The optimized response maintains Q >= 30 from 16 to 49 GHz.

Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear and state-dependent mechanisms. Linear models are interpretable but may miss these effects, while deep neural networks improve forecasting at the cost of transparency. We use neural networks as tools for structural discovery rather than only for prediction. A deep feed-forward network is trained on high-frequency returns and signed volumes for large- and small-tick stocks and compared with a linear VAR benchmark. The neural network improves predictive performance, especially for returns, revealing nonlinear dependencies beyond the linear specification. Using Shapley-based explainability, we show that the dominant contributions are concentrated at the most recent lags. Model-implied responses are consistent with conditional averages reconstructed from the data. Unlike empirical averages, however, the neural-network decomposition isolates individual regressor contributions to the aggregate dependence. Lagged signed volume generates sign-preserving and saturating effects, consistent with nonlinear price impact and order-flow persistence. Lagged returns act as state variables: when the previous trade does not move the price, the model predicts continuation in the direction of past order flow, whereas non-zero returns generate attenuation or reversal. Building on these findings, we introduce a parsimonious SHAP-inspired nonlinear parametric model. It reproduces the main return-volume dependencies, outperforms the linear VAR benchmark, and achieves performance comparable to the neural network. A multi-lag extension captures residual longer-memory effects while preserving interpretability. Overall, explainability offers a route from black-box prediction to economically meaningful parametric models of price and trade dynamics.

In this work, we investigate surface polaritons (SPs) arising at the interface between a simple dielectric and a bi-isotropic medium, in the lossless regime and in the presence of small losses. For the former, non-Dyakonov surface polaritons can arise for positive permittivities and magnetoelectric parameters. General restrictions on the permittivities and bi-isotropic parameter (existence conditions) are derived. For the latter, the effect of small losses is considered, and the characteristic surface plasmon-polariton lengths are discussed. The propagation length can exhibit a local maximum for specific values of the magnetoelectric parameter, an unusual new behavior that can be regarded as a signature of these chiral surface polaritons. Surface waves in an interface involving a bi- isotropic medium with an anomalous Hall effect (AHE) are also examined. Multiple configurations for their occurrence are obtained, revealing a richer scenario with distinct regimes of propagation and coexistence in the parameter space. The presence of a non-null AHE contribution creates a magnetoelectric range for which the surface wave propagation is forbidden, providing an additional signature of surface-polariton propagation in bi-isotropic media with an AHE contribution.

The Fast Interaction Trigger detector of the ALICE apparatus at the LHC includes a Cherenkov subsystem utilizing 52 Planacon XP85002/FIT-Q microchannel plate-based photomultiplier tubes. It is a precise trigger, multiplicity and luminosity detector located in the forward region, where the flux of collision products is the largest. The photon load reaches up to 2*10^8 photoelectrons / cm^2 / s for the innermost photosensors, corresponding to 0.4 {\mu}A / cm^2 average anode current density. The lifetime-extended version of the Planacon MCP-PMT with atomic layer deposition cannot handle such a large anode current. We therefore use the non-ALD version of the Planacons that, on the other hand, suffer from a limited lifetime in terms of the integrated anode charge. Here we present the long-term performance trends of the non-ALD Planacons up to the integrated anode charge of 2 C / cm^2. We demonstrate that the low-gain operation strategy allows to maintain excellent time resolution in multiphoton mode unchanged up to that large integrated charge. While the majority of the equally illuminated MCP-PMTs feature almost identical ageing speed, two outliers suffering from a faster ageing coincide with the outliers in the noise characteristics measured prior to the irradiation. Moreover, we report that the effective ageing speed is partially suppressed by the self-recovery of the response of aged MCP-PMTs newly observed by our group during months-long no-beam periods.

We present a combined experimental and Floquet-theoretical study of avoided crossings in the AC Stark-shifted electromagnetically induced transparency (EIT) spectra of Rydberg atoms. Observed avoided-crossing structures cannot be fully explained by conventional AC Stark maps alone, which provide the energy shifts but do not reveal the underlying state composition and coupling pathways. Using the Shirley method, we analyze the Floquet eigenstates to establish a direct connection between the measured spectra and the underlying dressed-state dynamics. By tracking the evolution of state mixing, bare-state composition, and dominant higher-order Floquet coupling pathways, we reveal the physical mechanisms responsible for the formation of avoided crossings across different principal quantum numbers and RF frequencies. Our results demonstrate that Floquet-eigenstate analysis provides a powerful framework for interpreting complex AC Stark-shifted Rydberg-EIT spectra and the underlying higher-order Floquet coupling interactions.

Neural quantum states (NQSs) have emerged as a powerful ansatz for quantum dynamics. Their high entanglement capacity promises to overcome the entanglement barrier. However, existing time-dependent NQS methods are ill-equipped to propagate sparsely supported initial states due to the inherent mismatch between the supports of such states and their time derivatives. To address this issue, we introduce the interpolation sampling method as a novel form of importance sampling, where samples are drawn from a distribution that interpolates between the wave function and its time derivative. Combined with a global-in-time variational principle, we demonstrate with the example of the two-dimensional transverse-field Ising model that interpolation sampling allows for accurate propagation of extremely peaked states that standard wave function sampling could not tackle. We further improve sampling efficiency by introducing (i) a configuration-time joint sampling scheme where spin configurations and time are both treated as random variables, and (ii) a hybrid strategy for sample proposal that incorporates the knowledge of a Krylov subspace. Our work extends the scope of time-dependent NQS methods to a wider range of physically relevant scenarios, while challenging the prevailing reliance on the Born distribution and its close variants as the default basis for sampling.

The Bott-Duffin theorem shows that any arbitrary positive real rational function of finite order, representing a passive network, can be represented by a finite number of positive value resistors, capacitors and inductors. It is shown here that if the positivity restriction is lifted, any rational function or polynomial of any order may be represented by a network of (possibly negative) inductors, capacitors and resistors. When there are no repeated roots this is a sum of simple sub-circuits each of which contains no more than four components. When there are repeated roots a more complex circuit is required, here featuring iterated Wheatstone bridges of special form.

Quasicrystals lack the translational symmetry that underlies Bloch decomposition and Brillouin-zone integration, making full-band momentum-resolved electronic structure difficult to formulate and compute. We develop a systematically convergent reciprocal-space tight-binding framework for a broad class of quasicrystals. The method combines two systematically refinable components: a Fourier-module scattering-channel Hamiltonian that yields local spectral and current-current correlation quantities at each physical momentum, and an expanding hierarchy of pseudo-Brillouin zones that converts the resulting local quantities into bulk thermodynamic observables through an exact local-to-global relation. Applied to the Penrose and Ammann-Beenker models, the framework uncovers full-band momentum-resolved quasibands and a multichannel mechanism for pseudogap formation, both beyond the scope of low-energy effective models. It further resolves Zeeman-driven gap closings and reopenings, quantized Chern plateaus, and the phason invariance of bulk spectral and topological observables. This framework provides a unified reciprocal-space route to full-band spectral and topological properties of quasicrystals.

Quasicrystals occupy a distinctive position between the translational order of crystals and the disordered amorphous matter. Simulating this physics has remained challenging, since quasiperiodic structures lack the translational symmetry exploited for crystals and generally require costly diagonalization of large finite approximants. Quasicrystalline order admits two equivalent descriptions, a cut-and-project scheme from a higher-dimensional periodic crystal, and a discrete set of substitution rules acting on a finite alphabet. We show that the latter, written in a numeration system adapted to the substitution, defines a deterministic finite automaton with output, the digits of a site index are fed, and the automaton returns the letter occupying that site. We further exploit another equivalence to a different construction, the transition matrices are exactly the tensors of a matrix product state, whose bond dimension is the number of automaton states and is independent of system size. This allows efficient representation of extremely large tight-binding Hamiltonians in the tensor-train language, thereby yielding an exact matrix product operator for the quasicrystal Hamiltonian at any system size. We show how this framework works for two families of one-dimensional quasicrystals, the metallic-mean and $k$-bonacci families and we explicitly construct the Fibonacci, silver-mean, and Tribonacci quasicrystals. By leveraging efficient tensor-network compression and the kernel polynomial method, we compute spectral densities for chains with more than $10^9$ sites and directly resolve the hierarchical structure of the spectrum.