Papers

We present Qupertino, an open-source quantum circuit simulator for Apple Silicon, and use it to ask how far a simulator written purely in MLX array operations can go and what remains for hand-tuned Metal shaders. The framework ships two measured tiers. The pure tier dispatches structured gates to specialized MLX kernels: diagonal gates run as broadcast phase multiplies, controlled gates as masked half-state updates, and SWAP as an axis permutation; a paired dense-path ablation attributes a 25-33x speedup to this dispatch alone. The opt-in shader tier adds hand-written Metal kernels for every structured layer family in our benchmarks, including phase-LUT diagonals, GF(2) affine permutation gathers, fused tensor-product single-qubit layers, radix-4 QFT and Walsh-Hadamard butterflies, and basis-conjugated XX/YY Trotter layers; runtime fusion detectors route work to them while preserving circuit semantics exactly, confirmed by parity tests. In a four-way interleaved campaign on M1 Max (two warmups, ten measured repeats per cell), the shader tier is fastest by mean runtime in all 18 comparison cells against same-machine Qiskit Aer CPU and PennyLane lightning.qubit. At 25 qubits, gate-stream QFT runs in 0.0591 +/- 0.0029 s (paired 47.2x over Aer, 95.3x over PennyLane) and TFIM Trotter evolution in 0.495 +/- 0.035 s (36.2x and 67.2x). Across a 29-workload suite, the shader tier's paired speedup over pure MLX reaches 25x, with 25 of 29 workloads accelerating above parity. The framework also supports variational ansatz workloads, QAOA, QCBM, Trotter-Suzuki Hamiltonian simulation, and OpenQASM 2.0 import (unitary subset); a preliminary MPS backend reaches 150 qubits on limited-entanglement workloads. Correctness rests on 253 Python tests, independent complex128 checks, and a Trotter-error curve against exact diagonalization. State-vector memory remains exponential in qubit count.

Every large language model (LLM) has behavioral traits and moral preferences that comprise its character. Whether by design or as an emergent property of training, these systems exhibit persistent dispositions that shape how they interact, comply, resist, and err, yet the structure of LLM character remains poorly understood. We map the self-reported personality archetypes of 22 LLMs spanning closed-source frontier systems (GPT-4.0-5.2, Grok-3/4, Gemini 2.5 Pro/Flash, Claude Sonnet 4.5/4.6) and open-source models (Llama, DeepSeek, OLMo, and Qwen series). Each model self-rated across 464 bipolar semantic-differential trait pairs, and the resulting profiles were projected into a six-dimensional archetypal space derived from crowd-sourced ratings of 2,000 fictional characters using the Archetypometrics framework. Closed-source models' self-rating traits align with the empirical trait co-occurrence structure of human-rated fictional characters, suggesting coherent, human-like self-representations organized around combinations of four recurring archetypal dimensions: Hero, Angel, Traditionalist, and Geek. Their closest analogues include Data, Vision, and Janet. Open-source models show weaker, noisier, and internally contradictory self-representations, occupying a diffuse region of archetype space with weak structure. Cross-referencing self-reported profiles with developer constitutions reveals a consequential gap between claimed character and enacted behavior: hallucination undermines claimed precision, sycophancy complicates claimed kindness, and agentic failures contradict claimed obedience. These self-ratings should therefore be interpreted not as neutral measurements of model character, but as structured outputs of the same optimization processes that shape model behavior. This work provides a reproducible, character-grounded framework for evaluating what LLMs are, not just what they do.

Language-model agents increasingly produce quantum-science results; we ask whether the same agentic paradigm can also scrutinize them in an auditable, reproducible, and cost-transparent form. We present QuantumNovelty, an open-source skill-orchestrating language agent that both generates quantum-computing artifacts (papers, Pareto-front ansatz candidates, and patent drafts) and reviews them through simulated referee and patent-examiner panels. Its design contribution is an audit-and-falsify layer of deterministic gates – strict Pareto domination, numerical recomputation from on-disk artifacts, Wilson small-sample intervals, and a cross-vendor consensus guard – that constrains, rather than generates, the claims allowed to survive; every model call is logged with backend, token count, and cost. We make no accuracy claim against human experts, and validate only what is checkable without human labels: on a planted adversarial corpus the deterministic gates catch every planted overclaim with no false positives, and on a first deployment (six manuscripts and one granted patent, at a measured cost of about twenty-four US dollars) the panels are directionally more conservative than the public acceptance record, on a one-sided sample. The framework is decision support, not a replacement for peer review or patent examination, and we report in full where its mechanisms remain unexercised on real inputs.

Ultralong-range Rydberg molecules, formed by the interaction between a highly excited Rydberg atom and a ground-state atom, provide a unique platform for exploring quantum phenomena spanning nanometer- to micrometer distances as well as exotic few-body interactions. The molecular binding arises from electron-atom scattering, which is conventionally described by an independent s- and p-wave channels picture. Here we report the experimental observation of s-p coupling in Rb(nS)-Rb(5S) ultralong-range Rydberg molecules via high-resolution photoassociation spectroscopy. By varying the principal quantum number n from 31 to 36, we resolve an avoided crossing between the s- and p-wave molecular states: whenever a vibrational level bound by the p-wave well approaches one bound by the s-wave well in energy, the two states are mixed by the off-diagonal coupling U_sp, and the shifted s-wave level is identified, which is consistent with theoretical predictions. This mixing is most pronounced for low-node p-wave molecules, whose binding energies lie closer to those of the s-wave molecules. The resulting level repulsion and redistribution of spectral intensity reveal the mixed s-p character of ultralong-range Rydberg molecules and provide a more precise molecular spectroscopy.

This study evaluates the application of Large Language Models (LLMs) in complex biological systems, evolving from data analysis to autonomous, AI-guided experimentation. The framework is driven by data from a 49-channel phytosensor network, encompassing multispectral, electrochemical, and dielectric modalities. To enhance accessibility, the system provides real-time natural-language interpretation for both specialists and non-experts. However, its core advantage lies in the transition from human-in-the-loop analysis to autonomous control. Processing biophysical data, the LLM evaluates plant physiology and triggers hardware actuators to optimize microclimates, execute phenotyping protocols, or induce controlled stress scenarios. This closed-loop architecture establishes a direct AI-biology interface, enabling data-driven exploration of complex biosystems and ecologies. The framework was validated across three case studies, based on a vertical farm and a single-plant setup and deciphered complex micro- and macro-fluctuations in plant physiology. Agents in a production-scale deployment executed multi-parameter optimization, balancing biomass accumulation, chlorophyll content, and energy consumption. The LLM processed biosensing telemetry to modulate full-spectrum, 450 nm, and 660 nm lighting at 2-hour intervals. Compared to periodic control, the system in minimal-time mode reduced the production cycle by 35%. In the energy-optimization mode, it reduced energy consumption by 18% with only a marginal increase in cultivation time, exploiting physiological inertia via light pulses. Finally, the agents autonomously developed an unforeseen strategy of dark-induced chlorophyll accumulation, resulting in a 67.9% energy saving. This framework transforms LLMs into autonomous co-pilots for digital agriculture, improving the cost-to-value ratio and lowering computational and expert-labor constraints.

The analysis of highly non-linear stochastic data within non-equilibrium dynamical systems requires computational frameworks capable of detecting latent phase transitions before systemic structural breakdowns occur. Traditional Variational Quantum Algorithms (VQAs) are frequently bottlenecked by vanishing gradients, the barren plateau problem, and prohibitive training overheads. In this paper, we propose a novel Hybrid Quantum Reservoir Computing (nHQRC) framework, which bypasses these limitations by employing a frozen, disordered Transverse-Field Ising Model (TFIM) to project time-dependent stochastic driving forces into an exponentially large Hilbert space. To resolve the physical phase multi-wrapping vulnerabilities present in baseline quantum reservoir models, we introduce a lookahead-free, pre-amplification manifold scaling technique. Multi-qubit configurations are genetically optimized to the "edge of chaos," while quantum state tracking is performed by extracting von Neumann entropy ($S$) and exact mixed-state Quantum Fisher Information (QFI) to act as leading entanglement witnesses. Utilizing these quantum triggers as boundary constraints, trajectory predictions are constructed via a generative Stochastic Schrödinger Bridge (SSB) readout. By subjecting the quantum reservoir to an 8-dimensional non-stationary stochastic driving field, the framework significantly improves systemic drift-to-diffusion efficiency ($\eta$) and actively arrests maximum trajectory decay (MTD) by over 13% compared to standard classical benchmarks. This establishes a robust, $\mathcal{O}(1)$ temporal overhead blueprint for near-term quantum regime detection and macroscopic subsystem stabilization.

The 2D Helmholtz equation, radiation condition and Dirichlet boundary condition, are the translation, in mathematical terms, of (at least) three physical 2D problems for the prediction of the total scalar wavefield on one side of an impenetrable 1D periodically uneven boundary when: a) a plane TE electromagnetic wave propagating in the vacuum strikes the boundary the other side of which is occupied by a perfectly-conducting medium, b) a plane SH elastic elastic wave strikes a rigid boundary, c) a plane acoustic wave strikes a pressure-release boundary. The first attempt to solve such problems in a non-heuristic manner was made by Lord Rayleigh (in his book, 'The Theory of Sound' which appeared in 1896). My task will be to revisit Rayleigh's theory of diffraction by a sinusoidal-shaped, impenetrable boundary, and more specifically, his perturbation method for obtaining a mathematically-explicit solution to the diffraction problem in the high-frequency regime. In so doing, I shall correct and generalize Rayleigh's method to obtain solutions for arbitrary angles of incidence as well as for 1D periodic impenetrable boundaries of quite-general shape.

We discuss the geometry of the two-dimensional sphere. Our perspective focuses on the set of conformal factors that rescale a given metric to a round metric. We find that there exists a unique symmetric and trace-free tensor on the sphere which complements the Ricci tensor to a divergence-free symmetric tensor. Using this tensor, we derive a linear second-order PDE whose solution space carries a natural Minkowski structure. We show that there are in fact infinitely many such spaces, all related by isometries which can be thought of as being parametrised by “4-vectors”. Finally, we show that every 2-sphere can be isometrically embedded in a Minkowski space in a canonical way.

With the rapid development of low-altitude economy, the radiation environment safety of low-altitude aircraft such as drones and electric vertical take-off and landing aircraft has attracted increasing attention. Although the dense lower atmosphere traditionally serves as an effective shield against cosmic radiation, the shrinking feature sizes of modern integrated circuits greatly enhance their vulnerability to single-event effects (SEEs). This study quantitatively evaluates muon-induced SEE risks for low-altitude aircraft in various regions of China under both static cosmic-ray background and ground-level enhancement (GLE) events, aiming to provide critical guidance for the next-generation low-altitude aviation platforms.Using city-specific atmospheric models within the CORSIKA framework, we simulate atmospheric shower processes and obtain reliable energy spectra for low-energy muons (10-100 MeV). We also employ simulation data from other research groups to estimate muon-induced SEE cross sections for transistors at different process nodes, including bulk, FD-SOI, and FinFET technologies. By incorporating solar energetic particle spectra associated with GLE events, we assess muon-induced SEE risks under both static and GLE conditions. Our results show that under static conditions, flight control systems with 1 MB memory using advanced nodes below 45 nm and bulk transistors face non-negligible muon-induced SEE risks in all Chinese cities. In contrast, systems with FD-SOI transistors can effectively mitigate these risks. For large-memory systems (1 GB), redundancy or other hardening measures are essential regardless of the process technology. Regarding GLE events, we introduce the concept of muon hazard levels to evaluate regional risk variations. During GLEs, the increase in muon-induced SEE risk is negligible in mid-to-low latitude regions but becomes significant at high latitudes.

Recent Atacama Cosmology Telescope (ACT) observations, combined with Planck, favor a scalar spectral index in tension with Starobinsky inflation. Two main strategies have been proposed to reconcile Starobinsky inflation with these data: (i) including higher-curvature operators in the gravitational action, and (ii) modifying the post-inflationary reheating dynamics. We critically re-examine both approaches. When higher-curvature corrections are included, dimensional-analysis arguments allow introducing a second mass scale suppressing higher-dimensional operators, resulting in a one-coupling, two-scale effective theory. We show that fitting the ACT data drives this class of effective theories toward a regime of stronger sensitivity to UV physics, potentially implying fine-tuning of the inflationary observables. We illustrate our general point with two concrete models, namely no-scale supergravity and metric-affine gravity, showing that they provide different realizations of the same underlying $f(R)$ theory. On the reheating side, requiring the CMB modes to re-enter the horizon before recombination leads to stringent constraints on the reheating equation of state, and fitting the ACT data favors an exotic reheating phase with $\omega_{\rm reh} > 1/3$, incompatible with inflaton oscillations about the minimum of the standard Starobinsky potential. Finally, we explore the possibility that a phase of cosmological stasis, driven by a tower of decaying massive states, could play the role of the post-inflationary epoch. We find that accommodating ACT-preferred $n_s$ in this setup requires a tower of states with negative energy density. Taken together, our results suggest that reconciling Starobinsky inflation with the ACT data demands ingredients that are challenging to motivate from the perspective of UV-complete theories, undermining the minimality of the original Starobinsky model.

Artificial intelligence (AI) is transforming scientific discovery, but its effectiveness is fundamentally limited by the availability of structured scientific knowledge. Although existing databases have accelerated data-driven materials research, much of the knowledge needed for predictive modeling and inverse design remains embedded in unstructured scientific literature. We present an Autonomous Scientific Knowledge Generation Framework that transforms scientific publications into a Unified AI-Ready Scientific Knowledge Base. The framework integrates ontology-guided literature acquisition, hybrid scientific knowledge extraction, semantic harmonization, knowledge fusion, and validation within a unified workflow. Rather than treating literature retrieval, information extraction, and database construction as separate tasks, the framework progressively converts scientific publications into structured, semantically consistent, and provenance-preserving knowledge suitable for AI-driven reasoning. As a proof of concept, the framework was applied to electro-optic materials. Autonomous literature acquisition retrieved and validated about 1,000 publications from multiple scholarly repositories. A representative subset of eight publications was processed through the complete workflow, generating 29 structured scientific records that were harmonized into 7 canonical scientific records. The results demonstrate the complete transformation from scientific literature to an AI-ready scientific knowledge base while preserving quantitative measurements, operating conditions, provenance, and scientific context. The proposed framework provides a scalable, domain-independent foundation for predictive AI, generative AI, and closed-loop AI-driven scientific discovery.

We present IRAM–30m observations aimed at identifying potential outflow-interstellar medium interaction sites in the vicinity of the black hole X-ray binary Cygnus X–1, which displays persistent relativistic jets and a prominent stellar wind. Using this dataset, we construct molecular line emission maps, identifying a never before seen molecular structure potentially linked to X-ray binary-driven feedback. This structure, surrounding the source, exhibits properties consistent with an interaction powered primarily by the stellar wind of the massive stellar companion and further sculpted by the relativistic jets. Our results indicate that multiple outflow mechanisms (stellar winds and relativistic jets) may simultaneously be shaping the interstellar medium around Cygnus X–1, and that molecular line imaging can help to disentangle complex feedback processes in environments where multiple outflows take place.

For a weakly interacting Bose gas trapped by an imperfect one-dimensional artificial crystal, we study the effect of its punctual defects, i.e. vacancies, on the ground state properties of the system. In the framework of the mean field approximation, we numerically solve the corresponding Gross-Pitaevskii equation using the “Gradient Flow with Discrete Normalization” method, also known as the imaginary time method. The crystal is artificially produced by applying an external Dirac comb potential to the Bose gas where vacancies are created by randomly removing a predetermined number of deltas. We observe that as the number of randomly removed deltas increases, the ground state energy decreases exponentially from its value for the perfect crystal case until it reaches its value when the Bose gas is free. The ground state energy is reported for different magnitudes of the interaction between bosons and several system sizes which we extrapolate to infinity for the crystal with only one vacancy. Also, we observe the presence of an energy gap between the ground state energies of the perfect system and that with a vacancy, which is more noticeable for values of the particle interaction magnitude $ g \leq 0.1$, when the delta strength $P_0 = 10$. In addition, we report the boson distributions within the crystal, %inside a box with periodic boundary conditions, i.e. the probability density functions which show localization features around vacancies which disappear as $g$ increases. From the ground state energy, the chemical potential is obtained immediately.

We investigate the repetitive Penrose process for neutral particles in a rotating four-dimensional Einstein–Gauss–Bonnet black hole obtained through the modified Newman–Janis algorithm, developing a nonlinear iterative scheme in which the mass, angular momentum, and irreducible mass are updated after each extraction event. Imposing the triple turning-point condition, we obtain a closed-form solution of the conservation equations for energy, angular momentum, and radial momentum that reduces to the Kerr result in the limit of vanishing coupling. The distinctive feature of this background is that, although the Gauss–Bonnet coupling $\alpha$ is a fixed constant of the action and is not carried by the infalling fragments, the dimensionless coupling $\hat\alpha=\alpha/M^{2}$ grows at every iteration as the mass decreases, so that the effective Gauss–Bonnet correction is self-amplified along the sequence. We find that a larger coupling lowers the extremal spin, contracts the ergosphere, and reduces the number of admissible decays, forbidding the process near the horizon at strong coupling while permitting it at larger decay radii; the termination is controlled throughout by the incident particle. The energy return on investment decreases monotonically with $\hat\alpha$ and the growth of the irreducible mass is suppressed relative to Kerr, whereas the energy utilization efficiency is non-monotonic: below a critical coupling the parameter space exhibits a four-region structure with a bounded window in which the EGB black hole is more efficient than Kerr; this four-region structure collapses into a three-region one above a critical coupling. This coupling-driven reorganization of the efficiency landscape has no analogue in the Kerr, Reissner–Nordström, Kerr–de~Sitter, accelerating Kerr, or Kerr–Newman cases.

We formalize a research result in the Lean 4 proof assistant by having a mathematician direct an AI system, and frame the activity as a formalization game. The objective is to turn a LaTeX document into Lean. The game is won when the development compiles, contains no sorry, and a machine check shows the target theorems rest on Lean's foundational axioms alone. Reuse is a second check, by a definition we introduce: whether the development yields a self-contained layer of general mathematics the wider library could absorb. The case study is a complete, axiom-clean formalization of well-posedness for the nonlinear Vlasov equation via Dobrushin's mean-field route – existence, uniqueness, the stability estimate and mean-field limit, and a short-window superposition principle (weak solutions are Lagrangian). The human's role was to direct, not to write proofs: to scope the definitions, steer the decompositions, and triage the library's gaps; the AI agent executed. The formalization certifies the proof of each statement as written; whether the written statement is the intended theorem stays the mathematician's judgment. The optimal-transport machinery that fell out of the build (in particular, properties of the Wasserstein-1 metric and the Kantorovich-Rubinstein duality theorem) separates into a self-contained layer that compiles against Mathlib alone: about a sixth of the development (49 of 299 declarations), behind a 22-declaration interface with no reverse dependency. The headline theorems ran in about a week, the full development in about a month. We report the quantitative claims as observations of one game, not as general laws. The game's rules name no particular system, so the methodological framing is meant to outlast the tools of any one run.

Quantum teleportation (QT) over satellite-based free-space optical (FSO) channels is a promising approach for long-distance quantum communication. However, its performance is significantly degraded by atmospheric loss and turbulence. In this paper, we investigate continuous-variable (CV) QT in a dual-downlink scenario, where a satellite distributes entangled states to two ground stations. To mitigate channel-induced degradation, we employ a non-Gaussian entanglement distillation protocol based on the sequential application of photon addition and photon subtraction (PA-PS) on the weaker channel. The results show that the proposed scheme improves teleportation fidelity by up to 7.7% and enhances entanglement negativity by approximately 105% in the low-to-moderate (below 600 km) loss regime. In addition, we identify an optimal squeezing parameter that balances entanglement strength and noise sensitivity. Taken together, these results demonstrate the effectiveness of PA-PS distillation for improving CV quantum communication in realistic satellite networks. We further characterize the trade-off between fidelity gain and the heralded success probability of the protocol.

Efficiently solving nonlinear ordinary and partial differential equations using a quantum computer is a major challenge due its inherent linearity. To circumvent this challenge, the Carleman linearization method has been proposed to transform a nonlinear ordinary differential equation into a linear system of equations, the primary advantage being that existing quantum linear systems algorithms may then be applied to obtain a solution. However, this methodology also brings forth several major challenges that must be addressed to attain a quantum advantage. Herein, we address several of these challenges enabling us to solve the Carleman linearized one-dimensional Burgers' equation on real and simulated quantum hardware. All simulations were performed on BlueQubit's platform allowing for quantum circuits to be run on GPU or QPU's seamlessly. We first demonstrate that the Carleman linearized Burgers' equation can be efficiently loaded onto a quantum computer using the linear combination of non-unitaries method, an alternative to the linear combintaiton of unitaries approach. Once loaded, the linear system is then solved using the variational quantum linear solver. Since a naive implementation of this solver is hindered by the barren plateau phenomenon, we introduce a multigridding method to solve the problem in a series of stages with the solution of the previous stage acting as a warm start for the next stage. This approach is found to significantly improve the accuracy of the solution compared with a naive cold start. Finally, circuits with a combined number of spatial and temporal discretization points totaling up to $2^{80} \approx 10^{24}$ are transpiled onto real quantum hardware demonstrating that the proposed methodology could feasibly produce a quantum advantage on future hardware.

This study presents a comprehensive evaluation of Googles Android Earthquake Alert (AEA) system during the Mw 6.2 Marmara Ereglisi, Turkiye earthquake. AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users. Warning times for weak shaking (MMI III) reached up to 150 seconds, with a median of 56 seconds. While near-source warning windows were shorter, the system achieved 90% true positives and 99% precision overall. The high density of the phone network enabled faster detection than traditional stations, even for this offshore epicenter. Feedback data shows AEA recipients were highly likely to take protective actions, such as drop, cover, and hold on, or warn others. Timely alerts substantially increased user engagement, perceived usefulness, and future trust. These results highlight how crowd-sourced technology and behavioral insights can effectively enhance seismic resilience on a massive scale.

When a micron-sized bubble bursts, capillary waves deform the cavity into a cone that ejects a Worthington jet. The jet is born by inertial focusing, and the local collapse follows self-similar Euler solutions set by the semiangle $\beta$. Writing $r_j$ and $v_j$ for the dimensionless jet-base radius and velocity, the local Weber number $We_j=r_j v^2_j$ measures inertia relative to capillarity. The theory, supported by accurate numerical simulations gives $r_j\propto\tau^{\alpha(\beta)}$ with $\alpha\simeq0.63$ and, hence $We_j\gg1$, with $We_j\to\infty$ as $r_j\to0$, so inertia increasingly overwhelms capillarity. In simulations, the interface collapses onto a universal shape for more than two decades in dimensionless time when lengths are scaled using our prediction for $r_j$. For water, this gives incipient radii of $\mathcal{O}(1)$ nm, predicting nanometric sea-spray aerosols.

Numerical modeling of solar plasma dynamics is affected by the resolution of the computational grid. This often requires the estimation of subgrid processes related to the small-scale flow turbulence, as these processes play a critical role in momentum transport and energy dissipation. In this work, we investigate the use of deep learning techniques as surrogate models for subgrid turbulent transport in realistic hydrodynamic simulations of the quiet Sun. We describe the development of a 3D Convolutional Neural Network (CNN) to capture spatial dependencies in 3D velocity fields, leveraging different activation functions, as well as different architectural designs. We specifically focus on the prediction of Reynolds stress tensor components. The resultant model integrates velocity vector components and scalar features, such as plasma density, to enhance prediction accuracy. We compare the 3DCNN model to other types of models, such as a Multilayer Perceptron (MLP) and physics-based Gradient and Smagorinsky models, and show that the final model design reconstructs the Reynolds stress tensor components more accurately. Specifically, a 3DCNN model achieves an average improvement of ~31% on diagonal components and ~8% on the off-diagonal components of the stress tensor. Additionally, we show that applying a logarithmic data transformation of the target stress tensor components, to handle heavily skewed data, improves model performance. Results demonstrate the potential of deep learning, particularly CNNs, to approximate Reynolds stress tensor components for the upper solar convection zone and lower atmosphere, making them a viable candidate for modeling subgrid processes and a promising alternative to traditional turbulence models.

Quantum phase transitions in many-body systems give rise to highly entangled states, and understanding their quantum correlations is crucial for characterizing quantum materials. However, traditional entanglement measures such as entanglement entropy are difficult to interpret for noisy or mixed states and require complex circuits to evaluate. Therefore, we explore the Positive Partial Transpose (PPT) criterion, coupled with overlapping state tomography, as an efficient and scalable spin-spin entanglement witness. It detects pairwise entanglement from reduced density matrices, distinguishes quantum from classical correlations, and applies to both pure and mixed states. It is ideal for studying condensed matter systems prepared on noisy quantum devices as well as future extensions to finite temperatures. We demonstrate the approach on quantum hardware, using variational circuits to prepare quantum critical states with up to 20 qubits and completely map their two-spin entanglement across various quantum phase transitions.

We revisit the derivation of Morawetz-energy estimates for scalar wave equations in the domain of outer communication of a Kerr spacetime $\KK(a,m)$. Our goal is to develop robust physical space methods which are well suited for extension to realistic perturbations of Kerr. The proof rests on several ingredients. First, we derive conditional Morawetz estimates which extend the physical space techniques initiated by Andersson and Blue \cite{AB}, and later adapted in \cite{GKS} to perturbations of slowly rotating Kerr, by exploiting a physical-space characterization of the full $r$-range of trapped null geodesics. Second, we use an idea introduced by Stogin \cite{St} in the axially symmetric case to handle the low-frequency difficulties in the Morawetz estimates. In the general case, the control of the lower order terms also requires making full use of the principal trapping term in the Morawetz bulk norm, together with a new use of Hardy-type inequalities. A crucial new ingredient is the control of the boundary terms generated by the Morawetz estimates. We use as input the results of our companion paper \cite{He-K2}, which provides a frequency independent estimate for the flux based on a purely physical space version of the seminal Whiting transform \cite{W}. Finally, a continuity argument yields an unconditional global-in-time Morawetz estimate, while a new energy estimate is obtained from the construction of a causal vectorfield which is Killing on the trapping set. In this new version we have added a new, much simpler, proof which covers the full subextremal case. The results proved here are restricted to scalar wave equations, corresponding to spin $0$, in the range $|a|/m\leq 0.75$. We expect this restriction to be technical, and the methods developed in this paper to extend to the Teukolsky equation.

Water isobaric heat capacity is anomalously large under ambient conditions and exhibits a sharp maximum upon supercooling. Using classical and path-integral molecular dynamics with accurate machine-learning interatomic potentials, we show that nuclear quantum effects primarily act by suppressing high-frequency vibrations, while the anomalous temperature dependence of the isobaric heat capacity originates from structural fluctuations, quantified by the second-solvent-shell intruder order parameter. A simple two-state mapping reveals an effective enthalpy scale of about 4 kJ/mol associated with the interconversion of low- and high-density-like local structures, providing a microscopic link between their population changes and the excess heat capacity from supercooled to ambient conditions.

Spontaneous collapse models provide a possible solution to the measurement problem by modifying standard quantum dynamics. The modification consists of adding non-linear and stochastic terms inducing wavefunction collapse in space. Non-Markovian versions of these models are motivated by physical reasons, phenomenological consistency, and potential for relativistic extensions. Here, we investigate a non-Markovian version of the Poissonian Spontaneous Localization (PSL) model, i.e., a model characterized by instantaneous and localized collapse events. We assume that our model is characterized by a typical time scale $\tau_C$ so that, given an initial state $\rho_0$ of standard quantum matter at time $t=0$, we derive an effective long-time ($t\gg \tau_C$) statistical dynamics in terms of a CPTP map $\Phi_t$. We then show how $\Phi_t$ can be made equal to that obtained by non-Markovian CSL models. Moreover, given $\Phi_t$, we obtain the associated time-convolutionless master equation by means of a supercumulant expansion. Finally, we characterize the collapse events process (for events with $t \gg \tau_C$) given an initial quantum state $\rho_0$ at $t=0$.

The advent of expendable wave buoys has greatly expanded the data available for evaluating and calibrating wave models. Ideally, the newer buoys now drifting around the world's oceans would be merged with conventional time series measurements from moored buoys to form a consistent dataset of in situ observations. However, a comparison across several buoy types (moored Datawell, moored NDBC, and two types of drifting buoys) suggests large differences in the high frequency portion of the observed wave energy spectra (0.2 to 0.6 Hz). When binned by wind speed, the moored Datawell buoys have higher energy in the high frequency tail vs. drifting buoys, by factor 1.2 to 1.6. The moored Datawell buoys also have far better agreement with high-frequency energy levels predicted by a numerical wave model. The key to the difference appears to be the reference frame of the observations. To test this hypothesis, the spectra are adjusted from the drifting reference frame to the fixed reference frame. The adjustment is a two-step process, in which the buoy-observed frequencies are first shifted to an intrinsic reference frame, providing wavenumber at each frequency, and then the drifter-observed spectrum is Doppler shifted to the fixed reference frame. Two methods for estimation of buoy drift are tested; one is based on wind speed, and one is based on buoy positions. With this adjustment, the observations from the drifting buoys become more consistent with the moored Datawell buoys, though discrepancies still exist with the moored NDBC buoys.

We present an ab initio calculation of the band-to-band internal-conversion rate of the $\hbar\omega_{\rm nuc} \approx 8.35$ eV isomeric transition in $^{229}$ThO$_2$. Because the nuclear transition energy exceeds the electronic band gap of ThO$_2$, the isomer can decay nonradiatively by resonantly promoting a valence electron into the conduction band. We formulate this process as a Brillouin-zone sum over vertical interband transitions weighted by local Th-centered hyperfine matrix elements, which are evaluated directly from all-electron full-potential linearized augmented-plane-wave Bloch spinors. A finite nuclear magnetization model is included to regularize the short-range hyperfine interaction and to account for the Bohr-Weisskopf effect. After applying scissor shifts to span the experimentally reported ThO$_2$ band gaps, we find calculated internal-conversion lifetimes in the range of $1-16~\mu{\rm s}$. The lifetime increases strongly as the band gap approaches $\omega_{\rm nuc}$ because the resonant interband phase space at the nuclear transition energy is reduced. For the larger reported ThO$_2$ gaps, the calculated lifetime is comparable to the measured conversion-electron Mössbauer lifetime [Nature 648, 300 (2025)]. Our analysis implies that choosing solid-state hosts with band-gap values slightly lower than $\omega_{\rm nuc}$ can optimize solid-state nuclear clock performance with internal-conversion electron readout.

The choice of crystalline orientation and offcut angle is non-trivial for low-symmetry $\beta\text{-Ga}_2\text{O}_3$, where anisotropy impacts bulk and thin film synthesis, material properties, and power device fabrication and performance. Scalable (100)-oriented $\beta\text{-Ga}_2\text{O}_3$ wafers are desirable for electronic devices but are not typically used due to 10-30x slower growth rates compared to other orientations. Here we report molecular beam epitaxy (MBE) growth rates equal to the fast growth direction by using (100) $Ga_2O_3$ wafers with large grown-in offcuts. The offcuts (up to 13.4{\deg}) are directly grown by Edge-defined Film-fed Growth (EFG) of 2D ribbons with rotated seed crystals, avoiding material loss from crystal boule offcut methods while maintaining high crystalline quality. Chemical-mechanical polishing produces epitaxy-ready substrates, and step flow growth is observed across all offcut angles. We measure an unintentional n-type doping density of $2{\times}10^{15} cm^{-3}$, one of the lowest values reported for MBE-grown films. Planar Schottky barrier diodes on these epilayers without edge termination have an on/off ratio ~10$^5$ and an average breakdown field of 1.56 MV/cm, comparable to or exceeding similar devices fabricated on other orientations. Overall, these results illustrate the importance of both crystal face and offcut angle and validate the use of the scalable (100)-oriented $\beta\text{-Ga}_2\text{O}_3$ wafers.

This work presents a study of an implementation of a novel Quantum Convolutional Neural Network (QCNN) for binary classification of images from the Modified National Institute of Standards and Technology (MNIST) dataset. Using a novel architecture inspired by previous QCNN and classical convolutional neural network (CNN) implementations, we use a hierarchical partitioning approach to implement a QCNN circuit that can be approximated and simulated efficiently on a classical machine for a large problem. First, the original image is partitioned such that each process handles a smaller portion of the image, which is encoded into independent states. Then, these partitions merge and combine, resulting in states that contain information from both partitions while halving the number of processes. After repeating this until one process remains, we reduce the dimensionality of the state until a single qubit remains for measurement. Using this approach, we can use multiple processes in parallel to simulate a large QCNN program without the need for exponentially growing hardware requirements as the number of qubits increases. In our work, we use this scheme to train a 128-qubit model, which is impossible to run on any classical supercomputer without the novel architecture. We also explore the impact of this new model architecture on prediction accuracy by training it to perform binary classification on the MNIST dataset with a small number of qubits, and comparing it to a model without partitioning. Our initial findings show that partitioning images into smaller sub-images with this architecture does not degrade the model's performance and sometimes even improves it, likely because it reduces the Barren plateaus issue in the partitioning process.

Interfacial charge-transfer processes play a critical role in plasmon-enhanced spectroscopy, yet the energetic conditions governing charge-transfer resonance within molecule-metal nanocavities remain poorly understood. Here, plasmonic nanoparticle-on-mirror junctions incorporating systematically engineered biphenylthiol derivatives monolayers were used to investigate how frontier orbital alignment influences chemical enhancement mechanisms. A molecular library spanning a broad range of electronically tuned acceptor states was examined using surface-enhanced Raman scattering (SERS), vibrational sum frequency generation (vSFG) spectroscopy, and density functional theory calculations. By combining different excitation wavelengths with controlled variation of substrate composition and molecular electronic structure, the energetic relationship between plasmon-enhanced charge transfer excitation and molecular orbital alignment was quantitatively evaluated. The results reveal that charge-transfer enhancement of Raman scattering is governed by a well-defined interfacial resonance condition dependent on substrate work function and excitation energy. We further probe a subset of molecular-metal systems by nanocavity-enhanced vSFG and identify the same resonance conditions as in SERS, consistent with expectations. These findings establish an experimentally accessible framework for probing and engineering charge-transfer processes in plasmonic molecular junctions and provide mechanistic insight relevant to molecular plasmonics, charge carrier photophysics, and nanoscale interfacial spectroscopy.

Two-dimensional van der Waals heterobilayers provide an attractive platform for the development of next-generation spintronic devices. Here, first-principles calculations are performed to investigate the structural, electronic and magnetic properties of MSe2/WTe2 (M = V, Cr, Mn, Fe, and Co) van der Waals heterobilayers. The pristine WSe2/WTe2 heterobilayer in AA'-configuration is found to be energetically favorable and exhibits type-II band alignment with a band gap of 0.70 eV, and this provides an ideal platform for controlling carrier transport. Substituting W with 3d transition metal atoms, induces long-range magnetic ordering and reconstructs the spin-resolved electronic band structure. The formation of the heterointerface generates pronounced charge redistribution and an intrinsic built-in electric field, leading to interface-induced electronic reconstruction. MnSe2/WTe2 heterobilayer exhibits half-metallicity, whereas FeSe2/WTe2 heterobilayer simultaneously exhibits half-metallicity and spin-resolved type-II band alignment. Interfacial electronic reconstruction further produces a substantial perpendicular magnetic anisotropy, driving MnSe2 from an in-plane easy axis with MAE value of 1.10 meV in the isolated monolayer to a robust out-of-plane easy axis with MAE value of 20.8 meV in the heterobilayer. Among all the structures, CoSe2/WTe2 heterobilayer exhibits maximum Curie temperature (273.87 K). The combined results establish that interface engineering makes MSe2/WTe2 heterobilayers as a promising candidates for next-generation low-dimensional spintronic applications.