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Physics Physics

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Tuesday 28 April

Physics and Society

Two-Dimensional Structural Characterization of Music Genre Communities in Playlist Co-occurrence Networks

Makoto Takeuchi

Music genre classification shapes how listeners discover music, how platforms design recommendations, and how sociologists study cultural taste. Yet existing genre labels are inconsistent in granularity: they exaggerate boundaries between overlapping categories and hide sociologically important heterogeneity within broad labels. Cultural sociologists have long theorized that genres vary along two independent dimensions, boundary strength and internal differentiation, but existing empirical work has relied on fixed label sets, leaving these dimensions without quantitative operationalization from actual consumption behavior data. Here we propose a two-dimensional framework that extracts music communities bottom-up from playlist co-occurrence networks and characterizes each along two axes: external closure $B(C)$, measuring boundary strength relative to a random null, and internal differentiation $D(C)$, measuring organized internal subdivision. We validate the framework on two independent datasets across platforms, cultural contexts, and time periods, confirming that $B(C)$ and $D(C)$ are statistically independent and that each captures a distinct structural property. The framework reveals genre structures invisible to fixed labels: single labels splitting into communities with different boundary strengths, multiple labels merging into tightly bounded communities, and consumption spheres that no existing label describes. Comparison with prior theoretical predictions is broadly consistent, with the notable exception that Hip-Hop exhibits rich internal differentiation across both datasets, challenging its prevailing single-centered characterization. By providing a label-independent coordinate system grounded in listener behavior, this framework opens a path toward tracking how genre boundaries and internal structures evolve over time, a question that static label systems cannot address.
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Atomic Physics

Formation of gaseous, doubly charged cerium monofluoride CeF$^{2+}$ and its sensitivity to new physics

R. Simpson, C. Zülch, K. B. Ng et al. (+25 more)

Tricationic protactinium monofluoride ($^{229}$PaF$^{3+}$) has been proposed as a candidate for probing physics beyond the Standard Model of particle physics. Since studies with $^{229}$PaF$^{3+}$ require significant experimental advances, we exploit the stable, valence-isoelectronic dicationic cerium monofluoride (CeF$^{2+}$) as a surrogate. Gas-phase fluorinated-cerium molecular ions are formed and identified using the Off-Line Ion Source and TITAN mass measurement facilities at TRIUMF. Quantum chemical calculations are performed on the electronic structure of CeF$^{2+}$, revealing a parallel to that of $^{229}$PaF$^{3+}$. Moreover, these calculations provide estimates on the sensitivity of CeF$^{2+}$ itself to various $\mathcal{P,T}$-odd properties. A brief discourse on the specifics of the quantum control of CeF$^{2+}$ is presented which anticipates future searches for symmetry violations.
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All authors R. Simpson, C. Zülch, K. B. Ng, I. Belosevic, C. Charles, P. Justus, R. Berger, S. Malbrunot-Ettenauer, A. A. Kwiatkowski, M. P. Reiter, J. Ash, C. Babcock, J. Bergmann, E. Brisley, J. D. Cardona, C. Chambers, A. Czihaly, A. Gottberg, S. Kakkar, J. Lassen, F. Maldonado Milán, A. Mollaebrahimi, V. Radchenko, E. Taylor, A. Teigelhöfer, C. Walls, A. Weaver, P. Weligampola

Computational Physics · Cross-listed from cond-mat.mtrl-sci

Implementation of the hybrid exchange-correlation functionals in the SIESTA code

Yann Pouillon, Bill Clintone Oyomo, James Sifuna et al. (+6 more)

We present an efficient and accurate implementation of hybrid exchange-correlation (XC) functionals in the SIESTA code, enabling large-scale simulations based on Hartree-Fock-type exact exchange combined with strictly localized numerical atomic orbitals (NAOs). Our approach exploits a fitted representation of the NAOs in terms of Gaussian-type orbitals (GTOs), which allows for the analytical evaluation of four-center electron repulsion integrals (ERIs) via the LIBINT library. This framework is seamlessly integrated with SIESTA's real-space grid and sparse-matrix infrastructure, and is combined with multiple screening techniques to control the computational complexity. We also introduce a fully analytical formulation of hybrid-functional forces and a dynamic parallel distribution scheme that ensures excellent scalability. We validate our implementation through benchmark calculations on a broad set of systems (including semiconductors, insulators, and two-dimensional materials) and demonstrate that the HSE06 functional significantly improves the prediction of band gaps compared to PBE, in close agreement with G0W0 and experimental data. We analyze in detail the trade-offs between accuracy and computational efficiency as a function of the number of Gaussians, basis set range, and integral screening thresholds. Our results confirm that hybrid functional calculations in SIESTA are now feasible for large extended systems, making accurate first-principles predictions of electronic and structural properties accessible at scale.
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All authors Yann Pouillon, Bill Clintone Oyomo, James Sifuna, María Camarasa-Gómez, Xinming Qin, Carlos Beltrán, Fernando Gómez-Ortiz, Honghui Shang, Javier Junquera

Fluid Dynamics

Lacunarity in a Two-Dimensional Atomizing Liquid Jet

Guangnian Ji, Yash Kulkarni and Stéphane Zaleski

Atomization stretches and folds the liquid–gas interface before fragmenting it into ligaments and droplets, making fractal measures a natural descriptor of the lacunarity of the final breakup state. We examine this idea in two-dimensional (2D) VOF-DNS of a liquid jet with adaptive mesh refinement in Basilisk. Box-counting of the full resolved interface does not yield a single scale-independent exponent. Instead, two scaling ranges appear, separated by a crossover near the box-counting level \(L_{\mathrm{box}}\simeq7\): coarser boxes measure the folded connected jet envelope, whereas finer boxes increasingly sample ligaments, droplets, and nearly smooth local interface segments. Decomposing the interface into detached droplets, ligaments, and the connected main body shows that the relevant effective dimension is structure dependent. Droplets are most lacunar with dimension below one, ligaments occupy an intermediate level, and the main body carries the largest coarse-scale dimension. This hierarchy persists over $Re_l=100$–$10^4$ at fixed $\We_g=200$. The lacunarity of droplets is reminiscent of that of the object positions in the night sky hypothesized by de Vaucouleurs.
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Instrumentation and Detectors

Machine Learning Enables Real-Time Waveform Decomposition for Dual-Readout Calorimetry

Liangyu Wu, Qibin Liu, Marco Toliman Lucchini et al. (+3 more)

Dual-readout calorimeters achieve superior energy resolution by simultaneously measuring Cherenkov and scintillation signals for event-by-event electromagnetic fraction correction, making them attractive for next-generation Higgs factories. However, if a full waveform readout is required for time-based analysis to separate Cherenkov and scintillation signals, high off-detector data rates might present challenges. These challenges can be mitigated by real-time signal processing in front-end electronics. We present a systematic comparison of machine learning (ML) and template fitting approaches for the separation of scintillation and Cherenkov light components in homogeneous dual-readout calorimeters across three representative crystal types. ML models achieve comparable signal extraction performance at lower sampling rates than template fitting. A single model trained over a range of incident particle energies demonstrates robust performance, and FPGA-compatible compression achieves latencies suitable for real-time application. This work establishes both baseline template fitting performance and ML-enhanced alternatives for crystal-based dual-readout calorimeters, offering practical pathways towards front-end feature extraction in future detector design.
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All authors Liangyu Wu, Qibin Liu, Marco Toliman Lucchini, Julia Gonski, Marcello Campajola, Stefano Moneta

Biological Physics · Cross-listed from cond-mat.soft

Kinetics of segregation of topologically-modified ring polymers in cylindrical confinement

Harsh Doshi, Shreerang Pande, Sathish K. Sukumaran et al. (+1 more)

In Escherichia coli (E. coli), entropic repulsion between the two daughter DNA ring polymers under cylindrical confinement is believed to be an important factor governing chromosomal segregation. The repulsion can be enhanced by topological modifications, i.e., by the introduction of internal loops at certain locations along the contour of the circular DNA. However, the effect of topological modifications on the rate of segregation of ring polymers remains unclear. Therefore, we systematically varied the number and the contour length of loops introduced at selected locations by crosslinking monomers. The appropriate crosslinking was motivated by observations that extruded loops are located mainly near the origin of replication (ori-proximal) region of the E. coli chromosome. This resulted in the chains becoming intrinsically anisotropic. Using Langevin dynamics simulations of these topologically modified bead-spring polymers, we calculated the time required for segregation under cylinder confinement. With certain caveats, we found that increasing the number of loops resulted in a decrease in the time of segregation. In line with past work, we propose that this is due to the increase in the entropic repulsion between the polymers upon increasing the number of loops. In addition to the number of loops, the contour length of the loops and the mutual orientation of the (anisotropic) chains in the initial configurations played a role in determining the time of segregation.
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All authors Harsh Doshi, Shreerang Pande, Sathish K. Sukumaran, Apratim Chatterji

Biological Physics

Orientation-Dependent Protein Binding at Nanoparticle Interfaces

Vigneshwari Karunakaran Annapoorani, Ian Rouse, Vladimir Lobaskin et al. (+1 more)

Accurate quantification of protein-nanoparticle interactions is essential for applications in nanobiotechnology, nanomedicine, and drug delivery. Motivated by recent computational and experimental work, we combine coarse-grained united-atom (UA) models with molecular docking to characterize protein adsorption on SiO_2 nanoparticles. We construct orientation-resolved heatmaps in which polar and azimuthal angles uniquely specify the relative protein-nanoparticle pose, and the map amplitude reports binding propensity via the minimum UA adsorption energy or the docking score. Each angular bin corresponds to a distinct docked complex, enabling systematic comparison of binding geometries across models. To relate docking score landscapes to Boltzmann-averaged UA adsorption energetics, we analyze eight birch pollen allergen proteins previously studied experimentally. Similarity between the two orientational distributions is quantified using the Jensen-Shannon divergence (JSD). We find encouraging agreement between the two approaches in several cases, while also identifying limitations and routes for improvement, including optimized angular resolution and iterative refinement of interaction parameters. Overall, this framework provides a quantitative bridge between coarse-grained energetics and docking outputs at protein-nanoparticle interfaces, supporting improved predictive modeling and mechanistic insight into protein-nanoparticle binding landscapes.
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All authors Vigneshwari Karunakaran Annapoorani, Ian Rouse, Vladimir Lobaskin, Nicolae-Viorel Buchete

Space Physics

Properties of the Stormtime Plasma Sheet at the Lunar Distance

A. Runov, V. Angelopoulos, A. V. Artemyev et al. (+1 more)

The electron fluxes at energies $E>$100\,keV are shown to be vanishing in the quiet time plasma sheet at geocentric distance of 60 Earth's radii (R$_E$) where the Moon traverses the magnetotail. Fluxes of energetic electrons up to relativistic energies were, however, observed during disturbed space weather conditions. In this paper, we study the data collected by the two lunar-orbiting Acceleration, Reconnection, Turbulence and Electrodynamics of Moon's Interaction with the Sun (ARTEMIS) spacecraft during their magnetotail traverses at two magnetic storm events. These observations allow us to compare plasma and field properties obtained at prior to storm and during the storm, including the storm recovery phase. We found that on the storms' recovery phases the average electron temperature increased by a factor of 4 compare to the pre-storm electron temperature. The ion temperature gain, however, did not increase a factor of 2. That leads to a decrease of ion to electron temperature ration to $\langle{T_i}/{T_e}\rangle\approx$3, in contrast to the pre-storm value of 7 to 9. We also found an increase in integral power of electrostatic fluctuations up to $\approx$2\,|mV/m|. Our observations suggest that the electrons were energized to energies $E>$100\,keV in the magnetotail. Although the exact mechanism of this energization remains unclear, we suggest that energization via continuous sporadic electron-only reconnection associated with electrostatic turbulence may be responsible for the anomalous electron energization.
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All authors A. Runov, V. Angelopoulos, A. V. Artemyev, X. An

Chemical Physics

DFT-assisted natural abundance 13C zero-field NMR via optical magnetometry

Blake Andrews, Xiao Liu, Raphael Zumbrunn et al. (+5 more)

Zero-field (ZF) nuclear magnetic resonance (NMR) spectroscopy probes scalar J-couplings between nuclei while dispensing with large homogeneous magnetic fields, enabling low-cost and geometrically flexible detection, including through conductive enclosures. Despite these advantages, its broader use for chemical analysis has been limited by sensitivity and by the difficulty of predicting the dense spectral multiplets that arise at zero field. Here we demonstrate natural-abundance (1.1%) 13C ZF spectroscopy on off-the-shelf liquids using a compact commercial 87Rb magnetometer for the first time, without hyperpolarization or special sample preparation. Instrumental advances yield improved sensitivity, <250-mHz linewidths and >week-long stability, enabling isotopomer-resolved fingerprint spectra across a 13-molecule library, including the ability to discern rare (0.0121%) doubly 13C-labelled species. In parallel, we demonstrate vibrationally corrected density-functional theory (DFT) based prediction of ZF NMR spectra for chemically diverse molecules with few-hertz accuracy. Comparing experiment with these calculations renders residual deviations as chemically informative, reporting on hydrogen bonding, hydration and ion pairing at high ionic strength. Together, these results contribute towards DFT-assisted ZF NMR as a general platform for field-constraint-free molecular identification and for extracting transient solution-state structure from responsive J-coupling observables.
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All authors Blake Andrews, Xiao Liu, Raphael Zumbrunn, Calvin Lee, Sahand Adibnia, Emanuel Druga, Martin Head-Gordon, Ashok Ajoy

Physics Education

A Pedagogical MKS-based Electromagnetic Unit Convention with $\epsilon_0 = \mu_0 = 1/c$

Alisher Sanetullaev

We propose a pedagogical, rationalized MKS-based convention for electromagnetic quantities designed to reduce cognitive load in undergraduate undergraduate electromagnetism. By setting vacuum constants to $\varepsilon_0 = \mu_0 = 1/c$, we preserve the familiar structure of Maxwell's equations while making the role of the speed of light explicit. In this convention, electrical units are expressed directly in terms of mechanical units (e.g.\ $[\mathrm{nuA}] = \sqrt{\mathrm{J/s}}$), effectively reducing the number of independent base units. A striking pedagogical consequence is that electrical resistance becomes dimensionless, capacitance and inductance acquire units of time, and radiation pressure reduces to $|\mathbf{E}\times \mathbf{B}|$, greatly simplifying dimensional analysis for circuits and fields. We introduce corresponding non-SI units (nu-units), provide conversion relations to SI, and demonstrate the potential utility of this system through comparative “before/after” derivations of the wave equation, electromagnetic energy density, radiation pressure, and the Bohr atom. Preliminary empirical support is provided by student attitude surveys administered to $N_1 = 46$ and $N_2 = 39$ students in an undergraduate physics course, which showed a statistically significant improvement in the perceived clarity of the wave equation derivation after exposure to the nu-system ($p = 0.005$, Mann–Whitney $U$ test), and a majority preference for the dimensionless-resistance feature.
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Plasma Physics

FPGA-Accelerated Real-Time Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference

Abhilasha Dave, Semin Joung, SangKyeun Kim et al. (+11 more)

In this work, we demonstrate the deployment of a hardware-accelerated machine learning (ML) inference system integrated into a real-time processing at the DIII-D tokamak fusion reactor. The team has successfully deployed an AMD/Xilinx KCU1500 field-programmable gate array (FPGA) into the realtime Plasma Control System (PCS) nodes that receives the live Beam Emission Spectroscopy (BES) signal used for Edge Localized Mode (ELM) forecasting. The FPGA hosts a dense neural network using the SLAC Neural Network Library (SNL) that has been trained to infer the likelihood of disruptive ELM conditions. This likelihood then feeds a separate plasma controller that uses Resonant Magnetic Perturbation coils to suppress the predicted disruptive condition. The SNL allows for on-the-fly updates of the neural network weights and biases without requiring full hardware resynthesis for the FPGA. Judicious design of the neural-network architecture can further allow for the hot-swapping of multiple classification tasks to be executed on the single FPGA, significantly enhancing the real-time adaptability of the system for context-aware control strategies that respond in real-time to evolving reactor conditions. These adaptive weights naturally support continuous model refinement and seamless task switching during live experimental operation. This use case is chosen as a high rate signal processing example that can serve as a template for general ML-based reactor diagnostic processing for active reactor control systems. We see this as an essential development for achieving reactor relevant operation in future continuous operation fusion devices.
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All authors Abhilasha Dave, Semin Joung, SangKyeun Kim, Ramon Reed, Keith Erickson, Jalal Butt, Azarakhsh Jalalvand, Mudit Mishra, James Russell, Larry Ruckman, Ryan Herbst, Egemen Kolemen, David Smith, Ryan Coffee

Computational Physics

Accelerating finite-element-based projector augmented-wave DFT calculations with scalable GPU-centric computational methods

Kartick Ramakrishnan and Phani Motamarri

Accurate large-scale Kohn-Sham density functional theory (DFT) calculations are essential for modeling complex material systems, including interfaces, defects, nanoclusters, and twisted two-dimensional heterostructures. Achieving chemical accuracy at scales of $10^4$-$10^5$ electrons with practical time-to-solution, however, remains challenging for existing DFT implementations. We present GPU-centric computational methods and algorithmic innovations within a finite-element (FE) discretized projector augmented-wave (PAW) formulation (PAW-FE) for accurate, efficient, and scalable electronic-structure calculations on modern exascale systems. The FE discretization, developed within a collinear spin formalism, accommodates generic boundary conditions and employs multi-resolution quadrature for accurate evaluation of atom-centered PAW integrals on coarse grids. The resulting generalized Hermitian eigenproblem is solved using residual-based Chebyshev filtered subspace iteration (R-ChFSI). Exploiting R-ChFSI's tolerance to inexact matrix-multivector products, we employ an approximate inverse PAW overlap matrix, mixed-precision arithmetic (FP32/TF32), and low-precision nearest-neighbor communication (BF16) during filtered subspace construction, along with block-wise computation-communication overlap to reduce cost while preserving robustness. These strategies yield up to $8\times$ and $20\times$ CPU-GPU speedups on Intel and AMD GPU architectures, respectively. Compared to plane-wave PAW methods, PAW-FE achieves close to 8$\times$ reduction in time-to-solution for 10,000-electron systems on NVIDIA GPUs, with larger gains at scale, and around 6$\times$ over norm-conserving FE approaches. We demonstrate scalability to 130,000-electron systems, establishing PAW-FE as an exascale-ready method for chemically accurate first-principles simulations.
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Plasma Physics

Non-thermal electron cyclotron emission during runaway plateau in tokamak disruptions from a highly anisotropic dielectric tensor

Yeongsun Lee, Kikyung Park, Tchanou Park et al. (+3 more)

During the runaway plateau phase in a tokamak, a cold background electron temperature of O(1 eV) forbids the onset of kinetic instability due to strong collisional damping. Nevertheless, non-thermal ECE anomalies at the level of 100 eV to keV have been observed in this phase without externally injected waves. To explore this, we characterize a highly anisotropic hot plasma medium with a Gaussian pitch-angle distribution. We derive an analytic hot plasma dielectric tensor, yielding direct expressions for the non-thermal emission coefficients and the kinetic instability drive rate. These analytic forms are verified against the KIAT and SYNO codes at small pitch-angle spread and are numerically complemented at large pitch-angle spread. Using the method of images, we define a fictitious global temperature of the entire plasma medium as measured by a horizontal ECE system. Because this representative medium temperature can exceed the keV level, the radiative temperature measured under incomplete wall reflection can be highly non-thermal without invoking any kinetic instability. This interpretation provides a conceptual basis for quantitative validation against experimental ECE measurements under realistic conditions.
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All authors Yeongsun Lee, Kikyung Park, Tchanou Park, Gunsu Yun, Yong-Su Na, Jong-Kyu Park

Instrumentation and Detectors

A new diffuse reflector filament for additive manufacturing of 3D printing finely-segmented plastic scintillator

A. Krech, S. Berns, E. Boillat et al. (+16 more)

This study presents the development and the characterisation of a novel white, reflective filament suitable for additive manufacturing of finelysegmented plastic scintillators. The filament is based on polycarbonate (PC) and polymethyl methacrylate (PMMA) polymers loaded with titanium dioxide (TiO$_2$) and polytetrafluoroethylene (PTFE) to enhance reflectivity. A range of filament compositions and thicknesses was evaluated through optical reflection and transmittance measurements of reflective layers made with the Fused Deposition Modelling (FDM) technique. A 3D-segmented plastic scintillator prototype was made with fused injection modelling (FIM) and tested with cosmic-ray muons to assess the light yield and the optical crosstalk. The results of this study demonstrate the feasibility of producing compact, modular 3D-printed scintillator detectors with a performance comparable to that of standard plastic scintillator detectors whilst exhibiting lower light crosstalk and higher light yield, owing to the improved optical properties of the reflector material.
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All authors A. Krech, S. Berns, E. Boillat, A. Boyarintsev, T. Dieminger, B.Grynyov, S. Hugon, N. Karavaeva, U. Kose, B. Li, S. Minenko, A. De Roeck, A. Rubbia, D. Sgalaberna, T. Sibilieva, M. Sibilyev, T. Weber, J. Wüthrich, X. Zhao

Optics

Near-field Meta-optics

Dongyoung Lee, Sehwan Kim and Jisoo Kyoung

Metasurfaces have revolutionized compact wavefront control using planar, subwavelength structures. However, conventional meta-optical devices predominantly operate within a far-field paradigm, assuming electromagnetic decoupling between the source and metasurface, which limits control to post-emission wavefront shaping. Here, we define and experimentally demonstrate near-field meta-optics - a regime where strong source - structure coupling enables simultaneous control of emission and radiation. By integrating an inverse-designed dielectric metasurface directly within the near field of a terahertz photoconductive antenna (PCA), we show that the metasurface co-defines the emission process itself. Our meta-PCA, incorporating a 50-um-high metasurface - one-third the thickness required by reciprocal far-field designs - collapses emission from ~60deg divergence to a sharp < 10deg forward beam, while enhancing on-axis intensity 50-fold compared to bare GaAs. Unlike far-field metasurfaces that typically trade efficiency for thinness while remaining laterally large, our device achieves extreme compactness in both dimensions. Remarkably, it exceeds the outcoupling efficiency of a bulky, millimeter-scale silicon lens by 10%, despite a volume reduction of over three orders of magnitude. These results establish near-field meta-optics as a transformative paradigm for developing high-efficiency, ultra-compact on-chip photonic systems across the electromagnetic spectrum.
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Physics and Society · Cross-listed from eess.SY

Optimal-Control Suggestion for Congestion on Freeways using Data Assimilation of Distributed Fiber-Optic Sensing

Yoshiyuki Yajima, Hemant Prasad, Daisuke Ikefuji et al. (+4 more)

This paper presents the optimal-control suggestion for congestion on freeways using data assimilation (DA) of distributed fiber-optic sensing (DFOS). To simultaneously maximize throughput and avoid/mitigate congestion, it is necessary to execute optimal control for the current traffic state as active transportation and demand management (ATDM) according to multi-objective optimization with real-time monitoring data. However, optimal control cannot be estimated due to intermittent observed data obtained from conventional sensors. To solve the issue, this paper proposes the ATDM optimal control estimation with DA of DFOS, which can monitor traffic flow in real time without dead zones. Our real-time DA method enables us to estimate the effectiveness of control scenarios by simulation. This paper also provides a method to uniquely determine the optimal-control solution among the Pareto solutions for multi-objective optimization. Throughput and mean speed across the entire road are considered as the objective functions. Variable speed limit (VSL) and inflow control are taken as ATDM examples. Validation results on a Japanese freeway show that (i) the optimal control scenario varies depending on the traffic state, especially congestion level; (ii) optimal control considering VSL alone improves throughput by 5-14% while the improvement rate for mean speed is 0-8%; (iii) throughput and mean speed are improved by 10-15% and 20-30%, respectively when VSL and inflow control are considered. This paper also implies the importance of balance management for the lane occupancy and proactive optimal control before congestion occurs.
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All authors Yoshiyuki Yajima, Hemant Prasad, Daisuke Ikefuji, Takemasa Suzuki, Shin Tominaga, Hitoshi Sakurai, Manabu Otani

Atomic Physics · Cross-listed from quant-ph

High-fidelity entangling gates and nonlocal circuits with neutral atoms

Simon J. Evered, Muqing Xu, Sophie H. Li et al. (+9 more)

Creation and manipulation of entanglement with low error is essential in quantum information systems. In practice, two-qubit entangling gates constitute a dominant error source, limiting circuit depths and performance in fault-tolerant architectures. Using a neutral-atom quantum processor, we realize entangling CZ gates with a high Rabi frequency smooth-amplitude pulse, employing state-selective readout and qubit reuse for fast calibration, and achieve state-of-the-art fidelities of 99.854(4)% which improve to 99.941(3)% upon loss postselection, with stable performance for 10 hours. We then use these low-error gates in quantum circuits with coherent atom rearrangement. We first benchmark performance by creating and disentangling cluster states, and subsequently implement scrambling circuits featuring longer-range connectivity to study non-locally entangled states generated through chaotic dynamics. These results pave the way towards deep-circuit, efficient fault-tolerant quantum computation.
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All authors Simon J. Evered, Muqing Xu, Sophie H. Li, Alexandra A. Geim, J. Pablo Bonilla Ataides, Marcin Kalinowski, Dolev Bluvstein, Nishad Maskara, Christian Kokail, Markus Greiner, Vladan Vuletić, Mikhail D. Lukin

Atmospheric and Oceanic Physics

Observation-Guided Neural Surrogate Learning for Scientific Simulation Emulation: A Single-Gauge Flood-Inundation Proof of Concept

Marzieh Alireza Mirhoseini

We present an observation-guided neural surrogate-learning framework for scientific simulation emulation, demonstrated on urban flood-inundation mapping. The framework combines LISFLOOD-FP hydrodynamic simulations with a real Gauge L stage record that is mapped to the simulation grid and converted to a datum-consistent local water-depth target before being used as single-site supervision. Focusing on a 256 x 256 crop around Gauge L in the Chicago metropolitan area, the method first constructs an ensemble-approximated Gaussian-process/local analogue surrogate (EnsCGP) to obtain a coarse flood-depth estimate and an uncertainty proxy. A U-Net-ASPP neural corrector then refines the coarse map using only simulation-derived and geospatial inputs: EnsCGP depth, the uncertainty proxy, rainfall, and spatial coordinates. The converted gauge-derived local depth is used only as a pointwise training target at the mapped gauge pixel; simulation-based losses are evaluated away from that pixel. Across temporally held-out events from 2013-2019, the emulator closely reproduces LISFLOOD-FP simulation targets outside the gauge-constrained pixel, with R^2 approximately 0.99 and mean absolute error below 0.01 m, and shows strong pointwise consistency with the converted Gauge L local depth target under the stated rolling-year protocol. We interpret these results as strong simulator-emulation agreement with pointwise observation-guided correction, not as independent validation of real-world inundation accuracy or as a complete operational flood-forecasting system.
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Optics

Optimized thermal control of a dual-wavelength-resonant nonlinear cavity

Fabian Meylahn, Henning Vahlbruch and Benno Willke

Optical resonator-enhanced nonlinear interactions are of great importance for the efficient generation of continuous-wave second harmonic generation, optical parametric oscillation, frequency mixing, and the generation of squeezed light. In order to maximize these interactions within the intra-cavity nonlinear material, high intensities, optimal phase matching, and simultaneous resonance of all interacting fields are required. However, the dispersion of the optical resonator often prevents the co-resonance of multiple wavelengths. Here, we present a novel implementation using a monolithic bimetallic heat sink for controlling the resonator dispersion based on a shallow temperature gradient directly applied to a section of the nonlinear crystal. This method enables precise dispersion control and is designed to minimize mechanical and thermal stresses in the nonlinear crystal, thus providing an additional method for designing highly efficient and reliable resonator-enhanced nonlinear devices for demanding applications such as gravitational wave detection, quantum optics, and frequency conversion.
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Computational Physics · Cross-listed from hep-ph

$\texttt{cuSkyrmion}$: A CUDA-OpenGL framework for interactive simulation and visualization of nuclei as Skyrmions

Sven Bjarke Gudnason and Paul Leask

We introduce $\texttt{cuSkyrmion}$, a 3-dimensional Skyrme model computation and visualization software, that is written in $\texttt{CUDA C}$ for rapid computation and visualization of especially the arrested Newton flow algorithm. The programme is interactive and lets the user construct Skyrmions either with configuration files, specifying coordinates, or simply in run-time using the keyboard and mouse. Rational map ansatz constituent Skyrmions can be inserted at any time and a random generator can produce a stochastic initial configuration. The software is composed into three main modules being a computational module, a rendering module and a main programme. The rendering/visualization module can readily be used by other computational modules and a $\texttt{Python}$-fork, $\texttt{skyrmion_solver}$, has been developed demonstrating the re-usability of the code.
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Classical Physics

Shear band patterns by boundary integral equations

Davide Bigoni and Domenico Capuani

Boundary integral equations are presented to analyze perturbations in terms of small elastic deformations superimposed upon an arbitrary, homogeneous strain. Plane strain deformations of an incompressible, prestressed, anisotropic, elastic solid are considered assuming the Biot constitutive framework. The special case of perturbations of stress/deformation incident wave fields, caused by a shear band of finite length formed inside the material at a certain stage of the deformation path, is formulated.
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Instrumentation and Detectors

Embedded underwater front-end electronics for the 3-inch photomultipliers in the JUNO experiment

Cédric Cerna, Miao He, Xiaoshan Jiang et al. (+598 more)

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kton liquid scintillator-based, low-radioactivity, multi-purpose neutrino detector located 693 meters (1800 m.w.e.) underground in the Guangdong province, China. To detect scintillation light produced in the target, the detector is equipped with 17,612 20-inch photomultipliers (PMTs), forming the Large PMT system (LPMT). In addition, 25,600 3-inch photomultipliers (the Small Photomultiplier System or SPMT) are deployed in the gaps between the LPMTs. This paper presents the design and performance of the underwater front-end electronics developed for the SPMT system. It details the individual electronics boards and their key components, the inter-board interfaces, the system-level design, and the firmware architecture that supports data acquisition and control. It also outlines mechanical and thermal integration, board validation procedures, and system performance metrics. The readout chain includes digitization of 128 PMT channels per unit, synchronized time-stamping, charge measurement, event packaging, and bandwidth management. Comprehensive validation confirms the system's readiness to meet JUNO's stringent physics goals. The underwater electronics achieve noise levels as low as 0.04 photoelectrons with minimal crosstalk (below 0.4%) and a bandwidth of 57 MB/s, ensuring reliable single photo-electron detection and operation under high-rate conditions. The SPMT system has now been fully integrated and installed in JUNO. Its commissioning and physics performance will be reported in a future publication.
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All authors Cédric Cerna, Miao He, Xiaoshan Jiang, Juan Pedro Ochoa-Ricoux, Frédéric Perrot, Angel Abusleme, Thomas Adam, Fengpeng An, Costas Andreopoulos, Giuseppe Andronico, João Pedro Athayde Marcondes de André, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Didier Auguste, Nikita Balashov, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Beretta, Antonio Bergnoli, Nikita Bessonov, Daniel Bick, Lukas Bieger, Svetlana Biktemerova, Thilo Birkenfeld, Simon Blyth, Manuel Boehles, Anastasia Bolshakova, Mathieu Bongrand, Clément Bordereau, Matteo Borghesi, Dominique Breton, Augusto Brigatti, Riccardo Brugnera, Riccardo Bruno, Antonio Budano, Jose Busto, Marcel Büchner, Anatael Cabrera, Barbara Caccianiga, Hao Cai, Xiao Cai, Yi-zhou Cai, Stéphane Callier, Antonio Cammi, Augustin Campeny, Guofu Cao, Jun Cao, Yaoqi Cao, Rossella Caruso, Cédric Cerna, Vanessa Cerrone, Jinfan Chang, Yun Chang, Tim Charisse, Chao Chen, Haotian Chen, Jiahui Chen, Jian Chen, Jing Chen, Junyou Chen, Pingping Chen, Shaomin Chen, Shiqiang Chen, Yixue Chen, Yu Chen, Ze Chen, Zhangming Chen, Zhiyuan Chen, Jie Cheng, Yaping Cheng, Yu Chin Cheng, Alexander Chepurnov, Alexey Chetverikov, Davide Chiesa, Pietro Chimenti, Ziliang Chu, Artem Chukanov, Gérard Claverie, Catia Clementi, Barbara Clerbaux, Claudio Coletta, Chenyang Cui, Luis Delgadillo Franco, Ziyan Deng, Xiaoyu Ding, Xuefeng Ding, Yayun Ding, Sergey Dmitrievsky, Dmitry Dolzhikov, Chuanshi Dong, Haojie Dong, Jianmeng Dong, Evgeny Doroshkevich, Marcos Dracos, Frédéric Druillole, Ran Du, Shuxian Du, Katherine Dugas, Stefano Dusini, Hongyue Duyang, Jessica Eck, Andrea Fabbri, Ulrike Fahrendholz, Lei Fan, Liangqianjin Fan, Jian Fang, Wenxing Fang, Elia Stanescu Farilla, Dmitry Fedoseev, Qichun Feng, Daniela Fetzer, Marcellin Fotzé, Amélie Fournier, Aaron Freegard, Ying Fu, Feng Gao, Alberto Garfagnini, Arsenii Gavrikov, Diwash Ghimire, Marco Giammarchi, Nunzio Giudice, Maxim Gonchar, Guanda Gong, Guanghua Gong, Yuri Gornushkin, Marco Grassi, Maxim Gromov, Vasily Gromov, Minhao Gu, Xiaofei Gu, Yu Gu, Mengyun Guan, Yuduo Guan, Nunzio Guardone, Rosa Maria Guizzetti, Cong Guo, Wanlei Guo, Hajar Hacine, Caren Hagner, Hechong Han, Yang Han, Chuanhui Hao, Vidhya Thara Hariharan, Miao He, Wei He, Xinhai He, Ziou He, Tobias Heinz, Patrick Hellmuth, Yuekun Heng, Rafael Herrera, YuenKeung Hor, Shaojing Hou, Fatima Houria, Yee Hsiung, Bei-Zhen Hu, Jun Hu, Tao Hu, Guihong Huang, Jinhao Huang, Junlin Huang, Junting Huang, Kaixuan Huang, Shengheng Huang, Tao Huang, Xin Huang, Xingtao Huang, Yongbo Huang, Jiaqi Hui, Lei Huo, Cédric Huss, Leonard Imbert, Ara Ioannisian, Adrienne Jacobi, Arshak Jafar, Beatrice Jelmini, Ignacio Jeria, Xiangpan Ji, Xiaolu Ji, Junji Jia, Cailian Jiang, Chengbo Jiang, Guangzheng Jiang, Junjie Jiang, Xiaoshan Jiang, Xiaozhao Jiang, Yijian Jiang, Yixuan Jiang, Xiaoping Jing, Cécile Jollet, Liam Jones, Narine Kazarian, Amina Khatun, Khanchai Khosonthongkee, Denis Korablev, Konstantin Kouzakov, Alexey Krasnoperov, Sergey Kuleshov, Sindhujha Kumaran, Nikolay Kutovskiy, Loïc Labit, Tobias Lachenmaier, Haojing Lai, Cecilia Landini, Lorenzo Lastrucci, Sébastien Leblanc, Victor Lebrin, Matthieu Lecocq, Ruiting Lei, Rupert Leitner, Petr Lenskii, Demin Li, Fei Li, Gaosong Li, Jiajun Li, Meiou Li, Min Li, Nan Li, Ruhui Li, Rui Li, Shanfeng Li, Shuo Li, Teng Li, Weidong Li, Xiaonan Li, Yichen Li, Yifan Li, Yingke Li, Yufeng Li, Zhaohan Li, Zhibing Li, Zi-Ming Li, An-An Liang, Jiajun Liao, Minghua Liao, Yilin Liao, Ayut Limphirat, Bo-Chun Lin, Guey-Lin Lin, Shengxin Lin, Tao Lin, Xingyi Lin, Jiajie Ling, Xin Ling, Ivano Lippi, Caimei Liu, Fang Liu, Haidong Liu, Hongbang Liu, Hongjuan Liu, Jianglai Liu, Jiaxi Liu, Jinchang Liu, Kainan Liu, Min Liu, Qian Liu, Shenghui Liu, Shulin Liu, Ximing Liu, Xuewei Liu, Yankai Liu, Yiqi Liu, Zhipeng Liu, Zhuo Liu, Lorenzo Loi, Alexey Lokhov, Paolo Lombardi, Kai Loo, Selma Conforti Di Lorenzo, Haoqi Lu, Junguang Lu, Meishu Lu, Shuxiang Lu, Xianguo Lu, Bayarto Lubsandorzhiev, Sultim Lubsandorzhiev, Livia Ludhova, Arslan Lukanov, Fengjiao Luo, Guang Luo, Jianyi Luo, Shu Luo, Wuming Luo, Xiaojie Luo, Vladimir Lyashuk, Bangzheng Ma, Bing Ma, Qiumei Ma, Si Ma, Wing Yan Ma, Xiaoyan Ma, Xubo Ma, Jihane Maalmi, Jingyu Mai, Marco Malabarba, Yury Malyshkin, Roberto Carlos Mandujano, Fabio Mantovani, Stefano M. Mari, Agnese Martini, Johann Martyn, Matthias Mayer, Davit Mayilyan, Yue Meng, Anselmo Meregaglia, Lino Miramonti, Marta Colomer Molla, Michele Montuschi, Iwan Morton-Blake, Xiangyi Mu, Lakshmi Murgod, Massimiliano Nastasi, Dmitry V. Naumov, Elena Naumova, Igor Nemchenok, Elisabeth Neuerburg, Alexey Nikolaev, Feipeng Ning, Zhe Ning, Yujie Niu, Hiroshi Nunokawa, Lothar Oberauer, Juan Pedro Ochoa-Ricoux, Sebastian Olivares, Alexander Olshevskiy, Domizia Orestano, Fausto Ortica, Rainer Othegraven, Yifei Pan, Alessandro Paoloni, George Parker, Yatian Pei, Luca Pelicci, Anguo Peng, Yu Peng, Zhaoyuan Peng, Elisa Percalli, Willy Perrin, Frédéric Perrot, Fabrizio Petrucci, Oliver Pilarczyk, Artyom Popov, Pascal Poussot, Ezio Previtali, Fazhi Qi, Ming Qi, Sen Qian, Xiaohui Qian, Zhonghua Qin, Shoukang Qiu, Manhao Qu, Zhenning Qu, Gioacchino Ranucci, Reem Rasheed, Thomas Raymond, Alessandra Re, Abdel Rebii, Mariia Redchuk, Bin Ren, Yuhan Ren, Cristobal Morales Reveco, Barbara Ricci, Mariam Rifai, Mathieu Roche, Narongkiat Rodphai, Fernanda de Faria Rodrigues, Aldo Romani, Bedřich Roskovec, Peter Rudakov, Arseniy Rybnikov, Andrey Sadovsky, Anut Sangka, Ujwal Santhosh, Utane Sawangwit, Michaela Schever, Cédric Schwab, Konstantin Schweizer, Alexandr Selyunin, Andrea Serafini, Mariangela Settimo, Junyu Shao, Anurag Sharma, Vladislav Sharov, Hangyu Shi, Jingyan Shi, Yuan Shi, Hexi Shi, Yike Shu, Yuhan Shu, She Shuai, Vitaly Shutov, Andrey Sidorenkov, Randhir Singh, Apeksha Singhal, Chiara Sirignano, Jaruchit Siripak, Monica Sisti, Mikhail Smirnov, Oleg Smirnov, Thiago Sogo-Bezerra, Sergey Sokolov, Julanan Songwadhana, Albert Sotnikov, Achim Stahl, Luca Stanco, Konstantin Stankevich, Hans Steiger, Jochen Steinmann, Tobias Sterr, Matthias Raphael Stock, Virginia Strati, Mikhail Strizh, Alexander Studenikin, Aoqi Su, Jun Su, Guangbao Sun, Mingxia Sun, Xilei Sun, Yongzhao Sun, Zhengyang Sun, Narumon Suwonjandee, Fedor Šimkovic, Christophe De La Taille, Akira Takenaka, Xiaohan Tan, Haozhong Tang, Jian Tang, Jingzhe Tang, Quan Tang, Xiao Tang, Yuxin Tian, Igor Tkachev, Tomas Tmej, Marco Danilo Claudio Torri, Andrea Triossi, Giancarlo Troni, Wladyslaw Trzaska, Andrei Tsaregorodtsev, Yu-Chen Tung, Cristina Tuve, Nikita Ushakov, Carlo Venettacci, Giuseppe Verde, Maxim Vialkov, Benoit Viaud, Vit Vorobel, Dmitriy Voronin, Lucia Votano, Pablo Walker, Caishen Wang, Chung-Hsiang Wang, En Wang, Hanwen Wang, Jiabin Wang, Jun Wang, Li Wang, Meng Wang, Meng Wang, Mingyuan Wang, Ruiguang Wang, Sibo Wang, Tianhong Wang, Wei Wang, Wenshuai Wang, Wenyuan Wang, Xi Wang, Yangfu Wang, Yaoguang Wang, Yi Wang, Yifang Wang, Yuyi Wang, Zhe Wang, Zheng Wang, Zhimin Wang, Apimook Watcharangkool, Junya Wei, Jushang Wei, Wei Wei, Wei Wei, Yuehuan Wei, Zhengbao Wei, Liangjian Wen, Jun Weng, Rosmarie Wirth, Bi Wu, Chengxin Wu, Qun Wu, Yinhui Wu, Zhaoxiang Wu, Zhi Wu, Michael Wurm, Jacques Wurtz, Dongmei Xia, Shishen Xian, Ziqian Xiang, Fei Xiao, Pengfei Xiao, Tianying Xiao, Xiang Xiao, Wei-Jun Xie, Xiaochuan Xie, Yuguang Xie, Zhizhong Xing, Benda Xu, Cheng Xu, Chuang Xu, Donglian Xu, Fanrong Xu, Jiayang Xu, Jilei Xu, Jinghuan Xu, Meihang Xu, Shiwen Xu, Xunjie Xu, Dongyang Xue, Jingqin Xue, Baojun Yan, Qiyu Yan, Taylor Yan, Xiongbo Yan, Changgen Yang, Chengfeng Yang, Fengfan Yang, Jie Yang, Kaiwei Yang, Lei Yang, Pengfei Yang, Xiaoyu Yang, Xuhui Yang, Yifan Yang, Zekun Yang, Haifeng Yao, Jiaxuan Ye, Mei Ye, Ziping Ye, Frédéric Yermia, Jilong Yin, Weiqing Yin, Xiaohao Yin, Zhengyun You, Boxiang Yu, Chiye Yu, Chunxu Yu, Hongzhao Yu, Peidong Yu, Simi Yu, Zeyuan Yu, Cenxi Yuan, Noman Zafar, Jilberto Zamora, Vitalii Zavadskyi, Fanrui Zeng, Shan Zeng, Tingxuan Zeng, Liang Zhan, Bin Zhang, Feiyang Zhang, Han Zhang, Hangchang Zhang, Haosen Zhang, Honghao Zhang, Jiawen Zhang, Jie Zhang, Jingbo Zhang, Junwei Zhang, Lei Zhang, Ping Zhang, Qingmin Zhang, Rongping Zhang, Shiqi Zhang, Shuihan Zhang, Tao Zhang, Xiaomei Zhang, Xu Zhang, Xuantong Zhang, Yibing Zhang, Yinhong Zhang, Yiyu Zhang, Yongpeng Zhang, Yuanyuan Zhang, Yue Zhang, Yumei Zhang, Zhenyu Zhang, Zhicheng Zhang, Zhijian Zhang, Jie Zhao, Runze Zhao, Shujun Zhao, Yangheng Zheng, Li Zhou, Shun Zhou, Xiang Zhou, Xing Zhou, Jingsen Zhu, Kangfu Zhu, Kejun Zhu, Bo Zhuang, Honglin Zhuang, Jiaheng Zou

Fluid Dynamics

Discovery of Sparse Invariant Subgrid-Scale Closures via Dissipation-Controlled Training for Large Eddy Simulation on Anisotropic Grids

Samantha Friess, Aviral Prakash and John A. Evans

Neural networks offer highly expressive turbulence closures, yet their complexity obscures the physical mechanisms they aim to model, and their computational cost can limit their tractability. To address these limitations, we introduce a sparsity-promoting subgrid-scale (SGS) stress closure modeling framework that identifies explicit polynomial model forms using sparse regression. Candidate models are constructed through scaling a minimal tensor basis by a truncated polynomial expansion of invariant scalars, thereby enforcing fundamental invariance properties while regulating the highest order of admissible terms. Arbitrary filter anisotropy is incorporated to enable consistent representation of turbulent structures across computational grids with anisotropic scales and resolutions. We also explicitly constrain SGS energy dissipation during training to improve functional performance and promote numerical stability. The framework is trained on a small dataset of idealized turbulence and evaluated through a series of a priori and a posteriori tests. Sensitivity studies examine the effects of variations in model order and optimization penalties for regularization and dissipation across a range of canonical flow configurations beyond those represented in the training dataset. We also evaluate on a separated flow benchmark to assess generalizability to a more complex turbulent regime. In many cases, the sparse regression closures achieve predictive accuracy comparable to an invariance-preserving neural network while retaining markedly simpler parametric forms. Moreover, we demonstrate that the sparse closures can be trained and evaluated at a fraction of the cost of the neural network model.
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Accelerator Physics

Revealing Laser and Electron Beam Evolution in 10-GeV-class Laser-Plasma Accelerators

H. Tang, A. Picksley, C. Benedetti et al. (+12 more)

Guiding relativistically intense laser pulses in low-density plasmas enables extended acceleration lengths in laser-plasma accelerators (LPAs), allowing for the production of multi-GeV electron beams. Quantitative interpretation of such experiments is often limited by substantial uncertainties in key plasma parameters, particularly the transverse density profile of hydrodynamic optically field-ionized channels. Distinct plasma density distributions can produce similar terminal beam energies, complicating efforts to infer the underlying interaction physics from measurements at the accelerator exit alone. By combining longitudinally resolved electron beam diagnostics with independent measurements of laser spectral evolution in a 10 GeV LPA, we establish a multi-observable constraint on plasma density profiles. Once plasma downramps are taken into account, excellent agreement is observed with simulation over the entire accelerator length for two plasma channel sizes. The validated simulations indicate that extending the accelerator length to 65 cm would increase the electron beam energy to 15 GeV. They also point the way to achieving $\sim$20 GeV electron beams in $\sim$70 cm via linear matching using the same 24 J laser energy.
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All authors H. Tang, A. Picksley, C. Benedetti, R. Li, H. E. Tsai, T. Mandal, E. Park, K. Nakamura, J. Stackhouse, D. Terzani, C. B. Schroeder, J. van Tilborg, J. Osterhoff, C. G. R. Geddes, A. J. Gonsalves

Applied Physics

A unified quantum random walk model for internal crystal effects in dynamical diffraction

Owen Lailey, Dusan Sarenac, David G. Cory et al. (+2 more)

The theory of dynamical diffraction (DD) in perfect crystals is the backbone of high-precision neutron and X-ray diffraction experiments, enabling accurate determination of crystal structure factors and the realization of perfect crystal interferometers. In practice, however, real crystals exhibit deformations and imperfections, including surface roughness, defects, temperature gradients, angled crystal faces, and curvature, that degrade interferometer performance and are difficult to model using conventional DD theory, particularly in complex geometries. To address these challenges, a quantum information (QI) model for DD has been under development, with demonstrated experimental agreement for both ideal crystals and in the presence of some imperfections such as surface roughness and defects. Here, we present a unified quantum random walk model that is now suitable for reproducing all established DD effects. We demonstrate this by incorporating a broad range of internal crystal effects influencing DD intensity distributions, including linear temperature gradients, the DD Talbot effect, and angled or miscut crystals. These results establish the QI model as a comprehensive and flexible framework for experimental analysis, as well as for the design of next-generation perfect crystal neutron interferometers and neutron optical components, such as condensing monochromators.
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All authors Owen Lailey, Dusan Sarenac, David G. Cory, Michael G. Huber, Dmitry A. Pushin

Computational Physics · Cross-listed from cond-mat.mtrl-sci

Predicting challenging phase transitions with Bayesian active learning

Lorenzo Bastonero, Gabriel Joalland, Chiara Cignarella et al. (+2 more)

Materials underpin modern technologies, from energy harvesting, storage, and conversion to information and communication technologies. Their functionality is often governed by the interplay between competing phases, as thermodynamic behavior shapes microscopic properties and ultimately determines technological performance; for instance, the light absorption of inorganic metal-halide perovskites in solar cells. Accurately predicting crystal thermodynamics, however, remains a major challenge for computational approaches because strong anharmonic effects require extensive sampling of the potential energy surface. Here, we present an on-the-fly Bayesian framework, combined with the stochastic self-consistent harmonic approximation, for learning first-principles interatomic potentials. This approach enables the prediction of thermodynamic properties over a broad temperature range with first-principles accuracy while requiring training on only a few tens to a few hundreds of atomic configurations. To demonstrate its power, we investigate the thermodynamic and dynamical properties of Li$_2$O, $\alpha$-CsPbI$_3$, and $\delta$-CsPbI$_3$, requiring only 44, 256, and 50 total-energy calculations, respectively. Notably, we show that this framework accurately captures the phase diagram of CsPbI$_3$, which explains its spontaneous degradation into the non-absorbing yellow phase, predicting the transition temperature with remarkable accuracy and efficiency. More broadly, the method presented opens a novel route toward accelerated materials engineering under realistic conditions for a wide range of technologically relevant applications, including solid-state batteries, optoelectronic devices, and memristors.
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All authors Lorenzo Bastonero, Gabriel Joalland, Chiara Cignarella, Lorenzo Monacelli, Nicola Marzari

Computational Physics · Cross-listed from quant-ph

Quantum-Inspired Robust and Scalable SAR Object Classification

Maximilian Scharf, Marco Trenti, Felix Bock et al. (+5 more)

SAR image classification naturally has to deal with huge noise and a high dynamic range particularly requiring robust classification models. Additionally, the deployment of these models on edge devices, such as drones and military aircraft, requires a careful balance between model size and classification accuracy. This study explores the potential of tensor networks to meet these robustness requirements, specifically evaluating their resilience to data poisoning. Unlike previous works that concentrated on conventional neural networks for SAR object detection, this research focuses on the robustness and model reduction capabilities of tensor networks in object classification. Our findings indicate that tensor networks are adept at addressing both the challenges of robustness and the need for model efficiency, thereby contributing valuable insights to the ongoing discourse in radar applications and deep learning methodologies in general.
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All authors Maximilian Scharf, Marco Trenti, Felix Bock, Padraig Davidson, Tobias Brosch, Benjamin Rodrigues de Miranda, Sigurd Huber, Timo Felser

Optics · Cross-listed from quant-ph

Numerically-Exact Quantum-Simulation Approach for Two-Dimensional Spectroscopy of Open Quantum Systems

Yi-Xuan Yao, Hao-Yue Zhang, Cheng-Ge Liu et al. (+3 more)

Two-dimensional spectroscopy (2DS) is a powerful ultrafast technique for probing electronic and vibrational dynamics in complex microscopic systems. Extracting detailed information on system dynamics and system-bath interactions from 2DS experiments requires precise theoretical simulations for comparison, which motivates the development of numerically-exact and computationally-efficient simulation approaches. Here, we propose a quantum-simulation approach for 2DS based on the bath-engineering technique (BET), which has been successfully employed in quantum simulations of open quantum dynamics. To demonstrate our approach, we first simulate the 2DS of a driven four-level system in chiral enantiodetection, where we also assess the applicability of the center-line slope (CLS) method for extracting time correlation functions (TCFs) from the 2DS. We further apply our approach to the 2DS of ${\rm Rh(CO)_2C_5H_7O_2}$ (RDC) dissolved in chloroform, where the results reproduce the main spectral patterns observed in experiments. Our work provides a numerically-exact and efficient framework for simulating 2DS, and can offer additional insight into the dynamics of open quantum systems.
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All authors Yi-Xuan Yao, Hao-Yue Zhang, Cheng-Ge Liu, Rong-Hang Chen, Qing Ai, Franco Nori

Applied Physics

Quantum sensing-enabled deuterium NMR spectroscopy with nanoscale sensitivity at low magnetic fields

Dileep Singh, Riley W. Hooper, Christoph Findler et al. (+2 more)

Nuclear magnetic resonance (NMR) spectroscopy provides unparalleled access to molecular structure and dynamics but is traditionally limited by weak signal strength, requiring large sample volumes and high magnetic fields. Here, we demonstrate nanoscale deuterium (2H) NMR spectroscopy using nitrogen vacancy (NV) centers in diamond, reproducing the characteristic quadrupolar powder line shapes that are present in the conventional bulk NMR spectra. By detecting statistical spin fluctuations from nanometer scale detection volumes, our approach delivers a sensitivity enhancement of six to eight orders of magnitude over inductive detection while operating at magnetic fields two orders of magnitude lower than those used in conventional NMR. Temperature dependent measurements of a deuterated polymer and molecular solid reveal distinct motional averaging and phase transitions with nanoscale sensitivity. Powder-like NV detected 2H NMR establishes a powerful tool for probing molecular dynamics on the nanoscale and, in the ultimate limit, at the single molecule level - capabilities beyond those of most existing spectroscopic techniques.
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All authors Dileep Singh, Riley W. Hooper, Christoph Findler, Utsab Banerjee, Dominik B. Bucher

Biological Physics · Cross-listed from cond-mat.stat-mech

Hierarchical Reconstruction of Time-arrow from Multi-time Correlations

Yijia Cheng, Ruicheng Bao and Zhonghuai Hou

The entropy production rate (EPR), a key measure of thermodynamic irreversibility in thermodynamics of small systems, is difficult to determine directly in experiments, motivating lower-bound-based estimation from observations. However, a systematic framework for organizing increasing amounts of the irreversibility information in experimental state observables into progressively tighter bounds remains lacking. Here, we show that multi-time correlations of a class of state observations naturally encode this information to provide a hierarchy. By defining a reconstruction operation as a combination of correlations, we obtain a sequence of lower bounds on the EPR. Correlations of higher order capture the thermodynamic information at greater temporal depth, thereby capturing more irreversibility and yielding tighter bounds. Under ideal conditions, this hierarchy converges to the full EPR in the limit of infinitely dense observations over a finite time window.
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