Atomic Physics · Cross-listed from cs.LG
QUIVER: Quantum-Informed Views for Enhanced Representations in Large ML Models
Aritra Bal, Michael Binder, Markus Klute et al. (+2 more)
Large machine learning models benefit substantially from
multimodal inputs that provide a complementary view of the same example. We introduce QUIVER (
QUantum-Informed Views for Enhanced Representations, a paradigm that enriches classical data-driven features with a
quantum Fisher view: a geometrically motivated, basis-independent summary of higher-order correlations captured by a variational
quantum circuit (VQC) trained to perform the same task. Unlike classical feature augmentation, the
quantum Fisher information
matrix encodes the intrinsic geometry of the learned
quantum state
manifold. While this feature map, motivated by
quantum information theory, is ordinarily non-trivial to model classically, it can surface statistical structure that additional classical data or model capacity finds difficult to learn. This makes the
quantum Fisher view a genuinely complementary modality rather than a redundant one. We demonstrate that QUIVER improves standard performance metrics on two
benchmark datasets from very different fields: QM9 for predicting molecule properties, and JetClass for predicting jet
flavor at the Large
Hadron Collider (LHC). The core contribution, however, is domain-agnostic: the
quantum Fisher view can be fused into a broad class of model architectures via targeted modifications to the base architecture, to incorporate information about the
quantum geometry of the problem. These results demonstrate that
quantum-
geometric features, extracted from simulated variational
circuits, can deliver measurable value for standard machine learning tasks, well before the advent of fault-tolerant
quantum hardware.
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All authors Aritra Bal, Michael Binder, Markus Klute, Benedikt Maier, Michael Spannowsky
Plasma Physics
Impurity-driven turbulence opens a pathway to ELM-free operation and enhanced pedestal stability in tokamaks
Santanu Banerjee, T. Macwan, A. Bortolon et al. (+7 more)
Edge-localized modes (ELMs) impose severe transient heat, and particle loads on
plasma-facing components, posing a critical challenge for steady-state operation of tokamak fusion reactors. Existing ELM control techniques either rely on externally applied perturbations or operate within narrow parameter windows, raising concerns for reactor scalability. Here we demonstrate that controlled injection of a low-Z
impurity can fundamentally modify pedestal transport and stability, enabling access to long ELM-free periods through
impurity-driven
turbulence. Using boron (B) powder injection in the DIII-D tokamak, we observe a progressive reduction of ELM frequency, culminating in long ELM-free phases. Pedestal stability analysis reveals a pronounced decoupling of peeling and ballooning stability boundaries at moderate B injection levels, opening a stability channel toward super-high confinement operation. At higher injection rates, long (~300 ms) ELM-free periods are achieved. Fluctuation measurements show that B injection selectively enhances low-frequency pedestal
turbulence, increasing inter-ELM particle transport and regulating pedestal gradients. The establishment of a feedback loop between
turbulence, particle transport, and the resulting modification of pedestal conditions, indicated by the observed hysteresis loop in the
evolution of density fluctuations in response to the B injection rate, is presented.
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All authors Santanu Banerjee, T. Macwan, A. Bortolon, R. Groebner, K. Barada, R. Maingi, T. Osborne, T. L. Rhodes, C. Chrystal, Z. Yan
Computational Physics · Cross-listed from cs.CV
From Local Training to Large-Scale Mapping: A Comparative Assessment of Machine Learning and Deep Learning for Transferable Satellite-Derived Bathymetry
Hsiao-Jou Hsu and Joachim Moortgat
Satellite-derived bathymetry (SDB) from multispectral imagery is cost-effective but scales poorly across regions, especially in optically complex coastal environments. We evaluate machine learning and deep learning for transferable SDB over the 0-20 m depth range using Sentinel-2 imagery. A Random Forest baseline and four CNNs (ResNet-50, ResNet-101, EfficientNet-B4, ConvNeXt-Large) are trained on Pratas Island and selected Great Barrier Reef regions, then evaluated on spatially independent intra- and cross-regional test areas. Preserving spatial continuity during training, by keeping contiguous reef blocks rather than random patches, is the single most impactful design choice; we further introduce a Smooth Weight Function (SWF)-weighted RMSE loss that emphasizes near-surface depths. With these choices, intra-regional RMSE ranges from 1.15 to 1.92 m over 0-20 m and is as low as 0.26 m for depths <= 3 m. Random Forest degrades sharply under cross-regional transfer (RMSE 1.53 m -> 2.99-3.78 m), while the deep models stay more robust (2.46-2.98 m). On the public MagicBathyNet aerial-RGB
benchmark (0-16 m) the proposed networks reach 0.19-0.22 m RMSE, outperforming a U-Net baseline and a task-specific
transformer architecture with substantially fewer parameters. We further exploit multi-temporal repeat imagery: training on it broadens diversity, and median-aggregating predictions across passes at inference reduces noise from changing sun angles, atmospheric conditions, water properties, and tides. We release optimized architectures and pretrained weights to enable scalable transfer to new sites.
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Optics · Cross-listed from hep-lat
Photonic Analog Quantum Simulation of (1+1)-Dimensional $U(1)$ Lattice Gauge Theory with Dynamical Matter
Nathan R. Gonzalez, Thea Budde, Klemen Kersic et al. (+5 more)
We propose a photonic scheme for analog
quantum simulation of a $U(1)$
Lattice Gauge Theory (LGT) with dynamical matter based on the Jaynes-Cummings-Hubbard (JCH) model. Here, an array of interacting cavities in the strong-coupling regime of cavity
Quantum Electrodynamics is mapped onto the alternating matter and gauge-field sites of the
spin-1/2
Quantum Link Model. In contrast to other analog LGT
quantum simulation methods, our approach implements the desired gauge-invariant dynamics through the hopping of polaritonic excitations among the array sites. The hopping is mapped to the gauge theory via precise tuning of polaritonic resonances in individual cavities. Using exact diagonalization, we show that the real-time
evolution of the JCH model accurately replicates that of a
Quantum Link Model. Finally, we discuss feasible routes to the beyond-classical simulation capability with scalable implementations in photonic and superconducting systems. This provides a novel route towards understanding the real-time dynamics of
lattice gauge theories with matter in higher dimensions.
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All authors Nathan R. Gonzalez, Thea Budde, Klemen Kersic, Zia Steele, Alex H. Rubin, Joao C. Pinto Barros, Marina Radulaski, Marina Krstic Marinkovic
Plasma Physics
Inductively Coupled Plasma Driven by Asymmetric Triangular Current Waveform
Shahid Rauf, Tianhong Wang, Jason Kenney et al. (+2 more)
Two-dimensional particle-in-
cell simulations of an inductively coupled
plasma (ICP) are used to investigate the influence of
radio-frequency (RF) current waveform and frequency on
plasma characteristics, collision processes, and the electron velocity distribution function driven by asymmetric triangular waveform current.
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All authors Shahid Rauf, Tianhong Wang, Jason Kenney, Dmytro Sydorenko, Igor D. Kaganovich
Atomic Physics
Sensitivity Enhancement of S-Band Rydberg Atom Microwave Receiver Using Resonant Cavity
Yipeng Xie, Xinbing Chen, Mingwei Lei et al. (+1 more)
Rydberg atom-based microwave electric field sensing has attracted growing interest owing to its inherent advantages, such as absolute calibration, wideband operability, and compatibility with room-temperature devices. A critical bottleneck that limits sensitivity is the inefficient coupling between the Rydberg atoms and the incident microwave field, particularly when detecting weak signals propagating in free space. Here we propose and experimentally validate a scheme that integrates a horn
antenna with a resonant microwave cavity to significantly improve this coupling for free-space signal reception in the S-band. Using a two-
photon excitation scheme in a cesium vapor
cell, we systematically characterize the sensing performance under three configurations: a bare
cell, direct cavity injection, and a cavity coupled to a horn
antenna that captures free-space microwave signals over a 1 m distance. In the
antenna-coupled cavity configuration, we achieve an optimal sensitivity of 2.33 nV/cm/$\sqrt{\text{Hz}}$ at the receiving
antenna, which corresponds to an enhancement of approximately 17.9 dB compared to the optimized bare vapor
cell configuration. Our findings offer a practical and effective route to boost the sensitivity of Rydberg atomic sensors, facilitating their adoption in real-world microwave metrology and wireless communication applications where weak free-space electric fields must be reliably measured.
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All authors Yipeng Xie, Xinbing Chen, Mingwei Lei, Meng Shi
Chemical Physics · Cross-listed from cs.LG
Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning
Vivin Vinod and Peter Zaspel
Machine learning has accelerated
quantum chemistry but is hindered by the prohibitive cost of generating high fidelity training data. Multifidelity machine learning (MFML) mitigates this overhead by systematically combining abundant low fidelity data with sparse high fidelity data. In spite of its success, standard MFML schemes rely on pre-defined scaling factors to determine sparse data ratio across fidelities, often generating redundant multifidelity data resulting in a loss of efficiency. Here, we introduce an adaptive on-the-fly multifidelity framework for machine learning that autonomously determines training
dataset composition. By dynamically querying training samples at each fidelity, the
algorithm saturates model accuracy at lower fidelities before moving up to more expensive reference calculations. We
benchmark the novel adaptive-MFML across diverse chemical properties including theoretical best estimate ab initio potential energy surfaces, golden standard coupled cluster energies, and the more chemically challenging excitation energies. In our numerical experiments we show that our adaptive
algorithm reduces data generation costs by up to a factor of 20 compared to single fidelity methods and improves upon standard MFML by up to a factor of 3. The mitigation of data redundancy establishes a high-accuracy low-cost pathway for sustainable cost-aware machine learning in
quantum chemistry.
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General Physics
Late-Time Cosmology and Structure Formation in Quadratic $f(Q)$ Gravity
G.G.L. Nashed, P.V. Tretyakov and A. Eid
We investigate the cosmological
evolution associated with the quadratic symmetric teleparallel
gravity framework, \( f(Q)=Q+\alpha Q^{2}+\beta \) where the relation \(Q\propto H^{2}\) generates an additional \(H^{4}\) contribution to the Friedmann equation. Using the exact algebraic solution for $H(z)$, we reconstruct the effective dark-energy sector and compare the background
evolution with $\Lambda$CDM using Type Ia
supernovae, BAO, and
cosmic-chronometer data. At the perturbative level, the model modifies the Poisson equation through a time-dependent effective gravitational coupling $G_{\textrm eff}(z)=G\big[1+\tfrac{2}{3}A E^{2}(z)\big]^{-1}$, where $A=18\alpha H_{0}^{2}$. For $\alpha>0$ this produces a weakened gravitational interaction, suppressing the linear growth factor $D(z)$, the growth rate $f(z)$, and the RSD observable $f\sigma_{8}(z)$. In the nonlinear regime, the reduced gravitational strength increases the spherical-collapse threshold and suppresses the
halo mass function, leading to a lower predicted value of $S_{8}=\sigma_{8}\sqrt{\Omega_{m}/0.3}$. Thus, the quadratic $f(Q)$ extension can reproduce mild deviations from $\Lambda$CDM at the background level while naturally alleviating the $S_{8}$ tension, offering a viable modified-
gravity explanation for recent observational hints of dynamical dark energy.
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Optics
Multilayer Babinet metamaterial to initiate nonreciprocal topological phenomena and generalized Faraday rotation
Balázs Bánhelyi, Miklós Waldhauser, Virág Szünstein et al. (+8 more)
Multilayers of Babinet complementary periodic structures constructed with miniarrays of spherical plasmonic nanoresonators were optimized to ensure Generalized Faraday Rotation. Nonreciprocal rotation and asymmetric transmission were achieved in spectrally overlapping regions due to the reach physics involving (i)
symmetry breaking via coupled localized modes, (ii) Brillouin zone-folding stemmed from constituent sub-
lattices forming in-plane twisted coupled loops, (iii) interlayer coupling between Babinet complementary patterns. The nanophotonical phenomena include (i) quasi-BIC resonances, (ii) hierarchically coupled localized and propagating modes that results in time-periodic Floquet modulation, (iii) initialization of synthetic potentials tuneable independently via intra and inter-layer parameters. The unique bianisotropic composites result in a synthetic vector gauge and emulated
magnetic field manifesting itself in tilted-precessing
magnetic dipoles and the accompanying modulation being time-periodic, inherently ensures a synthetic dimension. The asymmetric transmission is enhanced in the classical sense along quantized flat bands, and in mixed and forward bases inside finite wavelength-and-tilting intervals overlapping with nonreciprocal polarization rotation. The transmitted pulse re-shaping proves beating of nearby resonant modes, the loss can be compensated with active ad-layers thereby resulting in Faraday isolator capability. The multilayers synthetize topological phenomena in high-dimensional synthetic parameter spaces.
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All authors Balázs Bánhelyi, Miklós Waldhauser, Virág Szünstein, Ákos Sebők-Pap, Olivér Ardelán, Anna Kőházi-Kis, Dávid Vass, András Szenes, David Keene, Maxim Durach, Mária Csete
Atomic Physics
A Mid-Infrared Platform Based on Strontium Tweezer Arrays
Aaron Holman, Ximo Sun, Bojeong Seo et al. (+8 more)
Subwavelength atomic tweezer arrays, in which atoms can be positioned at distances smaller than their emission wavelength, have been proposed as a versatile platform to study collective emission phenomena, such as superradiance and subradiance. Experimentally, the realization of such arrays has been a challenge as typical emission wavelengths in the visible or near-infrared are short compared to typical tweezer spacings in the micrometer range. Here, we use $^{88}$Sr atoms in optical tweezer arrays to access a mid-infrared transition at 2,923 nm ($5s5p\:^{3}P_{2} \rightarrow\, 5s4d\:^{3}D_{3}$). We identify a magic trapping wavelength at 597.14(3) nm and demonstrate single-atom preparation and imaging with high fidelity. In addition, using 2,923 nm light, we demonstrate resolved-sideband cooling of tweezer-trapped strontium. Beyond enabling studies of collective emission phenomena in flexible arrangements of atoms, our platform opens novel opportunities for dipolar many-body physics and enhanced control over Rydberg dynamics and the strontium fine-structure
qubit.
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All authors Aaron Holman, Ximo Sun, Bojeong Seo, Joshua Corn, Zezheng Zhu, Yuan Xu, Jiahao Wu, Nanfang Yu, Dmytro Filin, Marianna Safronova, Sebastian Will
Instrumentation and Detectors
Full Characterization of a Mock Nuclear Waste Barrel with Muon Tomography using Micromegas Detectors
Raphaël Bajou, David Attié, Héctor Gómez et al. (+2 more)
Muon tomography based on multiple Coulomb scattering provides a non-destructive method to image dense and shielded objects using naturally occurring
cosmic-ray
muons. In the context of
nuclear waste characterization, we present the experimental imaging of a 205-L mock waste barrel using a dedicated 1m$^2$
muon scattering
tomography test bench. The system employs multiplexed resistive Micromegas detectors, enabling stable and high-precision
muon tracking. Monte Carlo simulations are first used to characterize material-dependent scattering signatures and to quantitatively assess identification performance using statistical reconstruction. These simulation-based results are then used to define objective discrimination thresholds, which are subsequently applied to experimental data for the localization and identification of internal anomalies. Using an Angle Statistics Reconstruction
algorithm, we achieve a spatial resolution of 10 mm and demonstrate the three-dimensional imaging of an internal structure containing both low- and high-radiation length materials. Material discrimination performance is evaluated using receiver operating characteristic analysis, yielding high identification efficiency for dense metallic inclusions such as lead and steel (AUC $\geq$ 0.96) within acquisition times of a few days, while cavities also exhibit strong contrast. Experimental results show good agreement with detailed Monte Carlo simulations. By establishing a continuous workflow from simulation-based performance characterization to practical application on measured data, this work provides a quantitatively validated framework for
muon scattering
tomography applied to complex, shielded objects.
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All authors Raphaël Bajou, David Attié, Héctor Gómez, Irakli Mandjavidze, Philippe Mas
Plasma Physics
Modeling Torque Induced Alignment in a Dusty Plasma System
Benny Rodriguez Saenz, Diana Jimenez Marti, Lorin Swint Matthews et al. (+1 more)
Irregular
dust aggregates immersed in
plasma sheaths experience several orientation-dependent torques that can modify their rotational dynamics and stability. Here, we investigate the rotational dynamics of charged irregular aggregates under conditions representative of a GEC rf
plasma cell using self-consistent numerical simulations. The aggregates rotate freely in a unidirectional sheath electric field that drives an ion flow, allowing the torque contributions acting on the aggregate to be evaluated throughout the motion. The results show that the sheath electric field is the main driver of rotation and aligns the aggregate electric dipole moment with the sheath field direction. The ion wake modifies this
alignment: its axial field component produces an opposing torque, while its transverse components introduce a destabilizing contribution that leads to small oscillations about the
equilibrium orientation. The rotational
equilibrium is described by an interaction energy well whose spring constant and depth increase with the sheath electric field magnitude, indicating stronger
alignment and greater resilience to angular perturbations at higher fields. A second order multipole expansion of the aggregate ion interaction shows that the dipolar term governs the ion contribution to the aligning torque, supporting a dipole ion approximation across the examined conditions. These results identify the sheath electric field as the principal stabilizing mechanism for irregular aggregate rotation and clarify how ion wake fields perturb the
equilibrium orientation.
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All authors Benny Rodriguez Saenz, Diana Jimenez Marti, Lorin Swint Matthews, Truell W. Hyde
Atmospheric and Oceanic Physics · Cross-listed from stat.AP
Probabilistic storyline attribution using machine learning
Frieder Loer, Maybritt Schillinger and Sebastian Sippel
A fundamental goal in
climate attribution is to estimate how forced
climate change contributes to observed extreme weather events. The storyline attribution method compares an observed weather event, conditional on its atmospheric dynamic state (i.e., atmospheric circulation), in the current, 'factual'
climate to an event with very similar circulation conditions in a hypothetical, 'counterfactual'
climate. However, physical
climate models cannot directly transfer these storyline counterfactuals across different
climate forcing states. Statistical and machine learning techniques may overcome this limitation; yet, emulating circulation-conditional extreme events under different
climate states is challenging. Here, we demonstrate distributional autoencoders (DAEs) as a versatile method for generating
climate counterfactuals. They model the full distribution of spatially resolved European temperature fields conditional on the atmospheric circulation state and the mean global warming level. These distributions allow for deriving meaningful conditional probability ratios, which is a particular advantage of the DAE-based storyline approach. We train DAEs on fully coupled
climate model simulations and we evaluate the modelled distributions across different factual and storyline-based counterfactual
climate model simulations. In an illustrative case study, we revisit the 2003 European heatwave and we generate counterfactuals for a hypothetical `2003-like European heatwave' using ERA5 circulation, which we hypothesize to occur a quarter century (2028) and a half century (2053) after 2003. The conditional intensity would increase from 29.3 {\deg}C in 2003, to 30.3 {\deg}C and 32.1 {\deg}C in 2028 and 2053, respectively and conditional probability ratios would be 2.1 and 3.2 when compared to 2003.
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Chemical Physics
Diagrammatic Monte Carlo for positron-molecule many-body theory
T. A. Scott, S. K. Gregg and D. G. Green
A diagrammatic Monte Carlo evaluation of the ladder series contributions to the correlation potential (self energy) of a positron in the field of a molecule is presented. The $GW$@TDHF, virtual-positronium ($T$-
matrix), and positron-hole Goldstone ladder series contributions are stochastically sampled order-by-order within the Tamm-Dancoff approximation, which is exact for the latter two classes, with Cesáro-Riesz resummation used to extrapolate to infinite order. Gaussian bases are employed and Coulomb
matrix elements are represented via density fitting, with the three centre integrals the largest arrays required to be stored in
memory. The
stochastic approach thus realizes a reduction in
memory of the largest arrays required on the order of the number of molecular orbitals in the basis $N\sim$10$^2$–10$^3$ compared to the exact deterministic solution of Bethe-Salpeter equations [J. Hofierka, B. Cunningham, C. M. Rawlins, C. H. Patterson and D. G. Green, Nature 606, {688} (2022)].
Benchmark results for lithium hydride show quantitative agreement with exact diagonalisation, notably demonstrating the successful
stochastic summation of the virtual-positronium infinite electron-positron ladder series.
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Atomic Physics
Suppression of differential light shifts in ground and metastable trapped-ion qubits
Drew Parks, Thomas Dellaert, Patrick McMillin et al. (+3 more)
In the presence of a
magnetic field, hyperfine clock
qubits can acquire a vector differential light shift that can be tuned via polarization to suppress the total differential light shift of high-power, off-resonant
laser light. We experimentally measure this "magic" polarization condition, suppressing differential light shifts in both the ${}^2\mathrm{S}_{1/2}$ ground and ${}^2\mathrm{F}_{7/2}^o$ metastable clock
qubits of $^{171}\mathrm{Yb}^+$. We present calculations of the minimum bias
magnetic fields required to suppress differential light shifts in the ground state clock
qubits of commonly trapped ion species, finding that they are below the strengths of fields already typically present in experiments. We further present methods for metastable clock-
qubit control in $^{171}\mathrm{Yb}^+$, demonstrating a state preparation and measurement infidelity of $2.9^{+3.0}_{-1.5}\times10^{-4}$ ($-35 \pm 4 \, \mathrm{dB}$).
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All authors Drew Parks, Thomas Dellaert, Patrick McMillin, Conrad Roman, Andrei Derevianko, Wesley C. Campbell
Physics and Society
A Guide to Higher-Order Homophily
Moritz Laber and Brennan Klein
Homophily, the overrepresentation of interactions among similar individuals, and heterophily, the elevated prevalence of interactions among dissimilar ones, are frequently observed mixing patterns in social networks. As hypergraphs are increasingly used to represent social systems, a higher-order perspective on homophily and heterophily becomes ever more relevant. Here, we provide two complementary perspectives on this problem: First, we survey measures that can be used to quantify homophily (or heterophily) in hypergraphs – emphasizing conceptual differences to existing pairwise measures – and explain each measure through in-depth examples. Second, we provide an overview of hypergraph models for higher-order mixing patterns, distinguishing several model families with distinct use cases. By providing a guide to existing methods and synthesizing the current body of knowledge on higher-order homophily and heterophily, we lay the basis for informed methodological choices and future developments.
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Optics · Cross-listed from quant-ph
Practical Limits on Integrated Squeezers
Devin J. Dean, Taewon Park, Lars S. Madsen et al. (+12 more)
Recent experiments have demonstrated the successful generation and detection of moderately squeezed
vacuum states with integrated photonics. However, in order to benefit from the reduced noise of highly squeezed light, many different noise sources must be mitigated. Here, we quantify the fundamental limits these noise sources impose on squeezing measurements and find surprising generality across different platforms and designs. We combine these different limitations into a simple model that provides practical guidance for the design and benchmarking of next-generation integrated squeezed-light systems.
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All authors Devin J. Dean, Taewon Park, Lars S. Madsen, Alex Terrasson, Sam Robison, Geun Ho Ahn, Ziyu Wang, Hubert S. Stokowski, Luke Qi, Jesse J. Slim, Joel Corney, Darwin Serkland, Warwick P. Bowen, Martin M. Fejer, Amir H. Safavi-Naeini
Geophysics
How geometry of subduction zones correlates with earthquake dynamics
Oscar Y. L. Chau, Rebecca Bendick, Gary P. T. Choi et al. (+1 more)
Subduction zones on the surface of the Earth, where abrupt sliding leads to earthquakes, are generally curved and localized. How does the geometry of these zones influence the occurrence of megathrust earthquakes? Here we use a combination of simple scaling arguments and data analysis using the differential geometry of surfaces to examine the relationship between the earthquake productivity of subduction zones and their shape. A scaling argument suggests how interface
curvature changes both the accumulation and release of stress relative to planar interfaces; conformable sliding along relatively flat subduction zones should lead to rare but large events, while curved subduction zones should lead to frequent smaller events. To test this, we leverage global geometry
datasets and analyze the correlation between the surface
curvatures of the subduction zones and the frequency and magnitude of earthquakes therein. Our analysis shows that weakly curved slab geometries are associated with rarer larger magnitude events, while slab geometries with a larger relative dispersion in
curvature are associated with frequent but smaller magnitude events. Using different scale-dependent shape metrics of the subduction zones, we show that the earthquake productivity is influenced by the conformability of the overriding and downgoing plates. More broadly, our results suggest the need to incorporate the large-scale geometry of subduction zones in computational models and predictive frameworks for earthquake risk.
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All authors Oscar Y. L. Chau, Rebecca Bendick, Gary P. T. Choi, L. Mahadevan
Biological Physics · Cross-listed from cond-mat.dis-nn
Frustrated neurons: Energy landscapes and relaxation dynamics in repulsive phase oscillators
Brandon B. Le
Geometrical frustration, a central paradigm in condensed matter physics, provides a unifying
language for systems in which locally preferred interactions cannot be made globally compatible. Here, we use this
language to formulate a minimal theory of frustrated neural timing, mapping repulsively coupled rhythmic units onto
antiferromagnetic XY models. Within this framework, the condensed-matter concepts of local constraints, degenerate ground-state
manifolds, metastability, and quench dynamics become a concrete diagnostic framework for structured neural phase dynamics. We analyze a hierarchy of geometries: a triangle as the minimal frustrated motif with two
chiral 120{\deg} timing states, a tetrahedron whose reduced ground-state
manifold consists of intersecting continuous branches associated with antipodal pairings, and a kagome
lattice on which local constraints define a constrained three-coloring
manifold. The kagome
lattice reveals the central dynamical result: zero-temperature relaxation suppresses global synchrony but typically selects low-energy metastable torque-balanced states rather than exact ground states. Finally, we show how the phase theory can be carried back towards biophysical neural models by treating it as an effective-interaction target, where geometrical timing frustration is realized through preferred phase lags that become incompatible around closed motifs. This perspective suggests that weak global coherence in neural systems does not necessarily signal disordered activity, but can reflect structured local timing order shaped by a frustrated dynamical landscape.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
Anand Babu, Rogério Almeida Gouvêa and Gian-Marco Rignanese
Inverse materials design is shifting materials discovery from forward prediction toward targeted proposal of candidates that satisfy objectives under physical constraints. Here, we review advances in generative
crystal structure modeling,
multimodal learning, and closed-loop design pipelines for crystalline solids. We survey how generators learn chemical-structural priors from databases to enable controllable sampling of periodic structures, comparing variational autoencoders, normalizing flows, autoregressive models, and
diffusion models. Across these families, we examine where feasibility constraints and physical priors enter, from representations and training objectives to sampling-time guidance, screening, and relaxation. We also discuss
multimodal learning combining
crystal structures, thermodynamic and electronic information, microscopy, spectroscopy, processing context, and scientific text to construct materials representations. Inverse-design strategies integrating conditional generation with latent optimization,
Bayesian optimization, reinforcement learning, and active learning are also examined. We highlight recurring failure modes, including surrogate exploitation, diversity collapse, distribution shift, and the stability-synthesizability gap, and outline evaluation based on validity, novelty, uniqueness, stability, and cost. To support credible claims, we define a nine-rung discovery-credibility ladder and propose a minimum reporting standard: declared matching tolerances and database snapshots; separate reporting of uniqueness, training-set memorization, and external rediscovery; novelty as a continuous distance distribution; energy-above-hull distributions with functional and hull version; relaxation-survival and dynamical stability rates; and validation cost per credible hit. Headline validity or S.U.N. rates without these disclosures should be treated as uninformative.
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Atomic Physics
Creating and Probing Spin-Squeezed States of Molecules
Connor M. Holland, Callum L. Welsh, Yukai Lu et al. (+4 more)
Polar molecules are a promising platform for
quantum-enhanced sensing and precision tests of fundamental physics, owing to their strong long-range dipolar interactions, broad sensitivity to electromagnetic fields, and sensitivity to potential physics beyond the Standard Model. However, the creation of metrologically useful entangled states in molecular systems has remained elusive. Here, we report the first observation of a class of metrologically useful entangled states -
spin-squeezed states - in polar CaF molecules trapped in an optical tweezer array. The
spin degree of freedom is encoded in rotational levels which are directly coupled by dipolar exchange interactions. By harnessing appropriate dynamical decoupling schemes we observe up to 3.0(3)dB of metrological gain, (2.2(3)dB without measurement correction) from direct exchange interactions. Using Floquet engineering, we further realize richer
Hamiltonians that preserve
spin squeezing while enabling the development of longer-range
quantum correlations. Using site- and
spin-resolved measurements we demonstrate that these entangled states enhance sensitivity to both homogeneous and spatially varying fields, and reveal strong non-classical correlations, including bipartite
entanglement and Einstein-Podolsky-Rosen steering. Finally, we transfer the
spin-squeezed states into long-lived and non-interacting hyperfine states, where the metrological enhancement persists for up to 100ms. Our results establish molecular optical tweezer arrays as a scalable platform for generating, controlling, characterizing, and storing entangled states of molecules, opening new opportunities for
quantum-enhanced sensing and precision tests of fundamental physics.
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All authors Connor M. Holland, Callum L. Welsh, Yukai Lu, David Wellnitz, Xing-Yan Chen, Ana Maria Rey, Lawrence W. Cheuk
Chemical Physics
Approximating Hartree-Fock theory via an efficiently local reformulation
Trine Kay Quady and Eric Neuscamman
We explore a reorganized framework for the Hartree Fock equations that allows varying patterns of locality to be imposed on the molecular orbitals while maintaining a highly efficient self-consistent field optimization
algorithm. Rather than limiting orbitals' spread and then variationally minimizing the energy within those limits, our reorganization neatly pairs each local degree of freedom with a specific solution condition that itself has a naturally local interpretation. These pairs can each be turned on or off, and, regardless of the sparsity pattern used to make such choices, the overall method maintains a fast self-consistent field optimization. We use this structure to test reaction-matched schemes for imposing orbital locality and, through Fock builds that exploit this locality, achieve competitive timings even in modestly sized molecules. Our initial tests also suggest that this approach to imposed orbital locality can be arranged so as to do minimal damage to both Hartree Fock and MP2 reaction energy predictions.
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Fluid Dynamics
Sharp-interface Simulations of Energetic Multiphase Flows with Large Density and Viscosity Ratios
Tzu-Yao Huang, Nicolas Valle, Artur K. Lidtke et al. (+2 more)
Flows with high density ratios, such as wave breaking and air entrainment in maritime applications, remain challenging to simulate due to their energetic and strongly nonlinear nature. In such regimes, maintaining numerical robustness is difficult when using the commonly adopted velocity-based formulation. The Consistent Mass-Momentum (CMOM) transport framework improves numerical robustness by enforcing fundamental physical properties, most notably momentum conservation and semi-discrete energy-conserving. However, CMOM replaces the advection of a continuous velocity field with that of a discontinuous momentum field. When combined with sharp interface methods, this leads to severe momentum shocks, for which conventional shock-capturing schemes are ineffective. To reconcile physical fidelity with numerical robustness, this work proposes a Synchronized Donor-Region of Momentum fluxes (SynDRoM) that enforces monotonicity of the transported velocity field. The resulting
algorithm effectively eliminates spurious velocity oscillations without sacrificing physical fidelity, as demonstrated through scalar transport and interfacial shear instability test cases. Beyond difficulties from large density ratio, improper estimation of viscosity in the vicinity of the interface can introduce numerical instabilities at finite time steps, thereby undermining overall robustness. To address this issue, a viscosity limiter based on the bounded kinetic viscosity concept is introduced and validated using a
gravity-driven plane shear flow. Finally, a breaking wave simulation is performed to assess the combined performance of the proposed physics-preserving numerical schemes for multiphase flows.
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All authors Tzu-Yao Huang, Nicolas Valle, Artur K. Lidtke, Kelli Hendrickson, Gabriel D. Weymouth
Chemical Physics · Cross-listed from cs.LG
Speculative Sampling For Faster Molecular Dynamics
Arthur Kosmala, Stephan Günnemann, Meng Gao et al. (+1 more)
Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system throughput with concurrent compute. To address this, we introduce Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampler for accelerating MD without adding relative error. Inspired by speculative methods in
language and
diffusion modeling, LSD uses a draft model to propose fast simulation steps and verifies them in parallel with a slower target model, applying a transport map from the draft to the target distribution. We extend speculative sampling to second-order Langevin dynamics, derive the achievable speedup as a function of physical parameters, show that LSD generalizes across different systems and draft-target combinations with a 3-9x speedup, and confirm theoretically and empirically that LSD samples trajectories from its target model distribution.
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All authors Arthur Kosmala, Stephan Günnemann, Meng Gao, Brandon Wood
Plasma Physics
Electron injection and acceleration into laser-driven wakefield from a solid overdense plasma target
M. Caetano de Sousa, S. Marini, M. Grech et al. (+3 more)
A
laser-
plasma acceleration scheme combining electron extraction from a solid overdense target with wakefield acceleration in an adjacent underdense
plasma region is presented. A
laser pulse excites a diffracted electromagnetic wave at the overdense
plasma interface, extracting and pre-accelerating electrons, which are then injected into
laser-driven wakefield cavities in the underdense
plasma. A parametric study identifies key conditions enabling efficient electron injection and energy gain in this two stage acceleration configuration. Two-dimensional particle-in-
cell simulations performed with the Smilei code show that the proposed scheme produces electron bunches with a tunable trade-off between charge, energy, and beam quality at
laser intensity $I_0 \lambda_0^2 \simeq 3.4 \times 10^{19}$ W$\mu$m$^2$/cm$^2$ ($\lambda_0=0.8 \mu$m). According to the parameters used, the electron beam is accelerated to peak energies of $\sim150-250$ MeV with an estimated charge in 3D of $\sim50-400$ pC integrated over the full width at half maximum energy range, and $\sim100-1800$ pC with energies above $50$ MeV.
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All authors M. Caetano de Sousa, S. Marini, M. Grech, S. Brunner, C. Riconda, M. Raynaud
Optics
Perturbative photonic matrix-vector multiplication with reduced phase-shift range
S. A. Fldzhyan, S. S. Straupe and M. Yu. Saygin
Programmable photonic meshes provide a promising platform for analog
matrix-vector multiplication, but their scalability is often limited by the large phase-shift ranges required in universal interferometer
circuits. We introduce a perturbative programming method that operates the
circuit near a fixed reference configuration and realizes the target transformation through interferometric subtraction, thereby reducing the required programmable phase excursion. We develop this approach for photonic
matrix-vector multiplication architectures based on universal
unitary meshes, and low-depth non-
unitary constructions based on sums of
unitaries. We identify favorable reference configurations through a local conditioning criterion, analyze the phase statistics obtained for random target
matrices, and show that perturbative programming produces phase distribution shrinking as the
matrix size increases. Identified reference point allows for the subtraction to be carried out electronically after detection, avoiding the interferometric subtraction loss. We further quantify the trade-off between reduced phase range and the intrinsic overhead introduced by the subtraction architecture, and show that for sufficiently lossy phase shifters the reduced phase range can compensate for this penalty. These results identify perturbative programming as a conditional but potentially useful route toward more scalable programmable photonic
matrix processors.
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Space Physics
Counterintuitive Magnetic Connectivity and Energetic Particle Flux Differences among Nearby Spacecraft During the 2023 February 24 Solar Energetic Particle Event
Weihao Liu, Xianyu Liu, David Lario et al. (+4 more)
For
solar energetic particles (SEPs), it is generally expected that observers magnetically closer to the eruption source region exhibit higher particle intensities than those poorly connected to the eruption site. However, the 2023 February 24 SEP event departs from this simple picture: Earth and STA, near 1 au, are nominally better connected to the source region, whereas
Solar Orbiter (SolO), at 0.77 au but less favorably connected, observed SEP fluxes more than an order of magnitude higher. This difference cannot be simply explained by nominal
magnetic connectivity or radial scaling of SEP fluxes alone. To investigate this behavior, we perform a global magnetohydrodynamic simulation of the associated coronal mass ejection (CME) using the Alfvén Wave
Solar-atmosphere Model-Realtime (AWSoM-R). The simulation reveals that the CME flux rope originates close to a coronal streamer and as it propagates and expands, the CME-driven shock is effectively distorted, developing into two distinct flanks with different strengths. Although the three spacecraft are separated by only $\lesssim$30$^{\circ}$ in heliolongitude, their
magnetic footpoints differ by $\gtrsim$50$^{\circ}$ in longitude because of a nearby stream interaction region. Specifically, Earth and STA connect to a weaker shock region, while SolO connects to the shock nose with a higher compression ratio and more efficient particle acceleration. We further simulate SEPs using the Multiple-Field-Line Advection Model for Particle Acceleration (M-FLAMPA) coupled with AWSoM-R, obtaining results that reproduce the observed flux differences among the three spacecraft, demonstrating that this counterintuitive behavior results from their connections to different regions of the inhomogeneous CME-driven shock.
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All authors Weihao Liu, Xianyu Liu, David Lario, Lulu Zhao, Tamas I. Gombosi, Alexander D. Shane, Igor V. Sokolov
Chemical Physics
DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
Tiancheng Li, Wentao Li, Anyang Peng et al. (+4 more)
Machine-learning interatomic potentials now approach
quantum-mechanical accuracy, but the most expressive equivariant architectures are costly to evaluate, and the leading ones depend on auxiliary denoising or direct-force pretraining. We introduce DPA4, an SE(3)-equivariant architecture spanning six size classes from 0.48 to 25 million parameters and reaching the accuracy of the strongest published models at several-fold to an order-of-magnitude higher inference throughput. Its convolution couples edge and node features across all angular degrees in an edge-local frame, and its Wigner bilinear nonlinearity is universal in its full form and, on an exact quadrature grid, equivariant to machine precision. On Matbench Discovery, DPA4 leads every ranked metric, and every variant evaluated lies on the accuracy–throughput Pareto frontier of the compliant leaderboard. DPA4-Pro attains the lowest energy error on OMat24 and lower total-energy and force errors than the strongest conservative baseline on the OMol25 composition-validation split. All variants are trained through the conservative energy-gradient path alone, made practical by a threefold-faster compiled implementation. DPA4 thus brings leaderboard-class accuracy within the routine compute budgets of molecular-dynamics and materials-screening workflows, for both inorganic
crystals and organic molecules.
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All authors Tiancheng Li, Wentao Li, Anyang Peng, Jianming Xue, Linfeng Zhang, Duo Zhang, Han Wang
Physics and Society
Evolved Collectives Combine Complex Internal Representations with Simple Outputs
Guilherme S. Y. Giardini, John F. Hardy II and Carlo daCunha
Collective intelligence emerges from local interactions among
agents with limited information, yet how internal controller organization relates to emergent collective order remains unclear. Here, we study evolved swarms with shallow neural controllers under explicit sensory and actuation constraints and compare collective order with hidden-layer complexity and output nonlinearity across 3024 conditions. Under these constraints, the most ordered regimes exhibit two simultaneous and seemingly contrasting effects: hidden-layer complexity increases, while the effective output mapping becomes more linear. The diversity of recurrent collective behaviors varies nonmonotonically across the control parameters, with pattern richness shaped by parameter-specific tradeoffs rather than a single generic constraint optimum. Unevolved controls show that output linearization persists without adaptation, whereas the hidden-complexity relation depends on optimization. These two effects are respectively consistent with the law of requisite complexity and ecological rationality, suggesting that adaptive collective intelligence can arise through a partitioned controller organization in which representational complexity and action-level linearization coexist within the same system.
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Applied Physics · Cross-listed from quant-ph
Optical Stability and Photophysics of NV Centers in Diamond up to 120 GPa
Kin On Ho, Cassandra Dailledouze, Vytautas Žalandauskas et al. (+9 more)
The nitrogen vacancy (NV) center has emerged as a powerful
quantum sensor in high-pressure research, with the observation of optically detected
magnetic resonance at megabar pressures. However, some aspects of NV physics require further investigation to optimize the development of NV-based sensing under pressure. Here, we study both experimentally and theoretically the optical properties of the NV center under hydrostatic pressure. We investigate the
evolution of the zero-
phonon line (ZPL) position, radiative lifetimes, optical lineshapes, and photoionization thresholds of the NV center under pressures up to ~120 GPa. We also provide spectroscopic guidelines for performing high-pressure optical experiments. Our results confirm that the NV center remains a robust
quantum sensor under extreme hydrostatic pressures, especially for
magnetic characterization.
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All authors Kin On Ho, Cassandra Dailledouze, Vytautas Žalandauskas, Grégoire Le Caruyer, Marek Maciaszek, Claire Roussy, Marie-Pierre Adam, Martin Schmidt, Loïc Toraille, Paul Loubeyre, Lukas Razinkovas, Jean-François Roch