Fluid Dynamics · Cross-listed from cs.LG
EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale
Yiheng Du and Aditi S. Krishnapriyan
Computationally resolving
turbulence remains a central challenge in fluid dynamics due to its multi-scale interactions. Fully resolving large-scale
turbulence through direct numerical simulation (DNS) is computationally prohibitive, motivating data-driven machine learning alternatives. In this work, we propose EddyFormer, a
Transformer-based spectral-element (SEM) architecture for large-scale
turbulence simulation that combines the accuracy of spectral methods with the scalability of the
attention mechanism. We introduce an SEM tokenization that decomposes the flow into grid-scale and subgrid-scale components, enabling capture of both local and global features. We create a new three-dimensional isotropic
turbulence dataset and train EddyFormer to achieves DNS-level accuracy at 256^3 resolution, providing a 30x speedup over DNS. When applied to unseen domains up to 4x larger than in training, EddyFormer preserves accuracy on physics-invariant metrics-energy spectra, correlation functions, and structure functions-showing domain generalization. On The Well
benchmark suite of diverse turbulent flows, EddyFormer resolves cases where prior ML models fail to converge, accurately reproducing complex dynamics across a wide range of physical conditions.
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Biological Physics · Cross-listed from cond-mat.stat-mech
On distinguishability among cell-division models based on population and single-cell-level distributions
Vikas, Rahul Marathe and Anjan Roy
It is well known that the different
cell-division models, such as Timer, Sizer, and Adder, can be distinguished based on the correlations between different single-
cell-level quantities such as birth-size, division-time, division-size, and division-added-size. Here, we show that other statistical properties of these quantities can also be used to distinguish between them. Additionally, the statistical relationships and different correlation patterns can also differentiate between the different types of single-
cell growth, such as linear and exponential. Further, we demonstrate that various population-level distributions, such as age, size, and added-size distributions, are indistinguishable across different models of
cell division despite them having different division rules and correlation patterns. Moreover, this indistinguishability is robust to stochasticity in growth rate and holds for both exponential and linear growth. Finally, we show that our theoretical predictions are corroborated by simulations and supported by existing single-
cell experimental data.
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Optics · Cross-listed from cond-mat.mtrl-sci
Development of a 10.8-eV Tabletop Femtosecond Laser with Tunable Polarization for High-Resolution Angle-Resolved Photoemission Spectroscopy
Jisong Gao, Qiaoxiao Zhao, Wenbo Liu et al. (+13 more)
The development of extreme ultraviolet sources is critical for advancing angleresolved photoemission spectroscopy (ARPES), a powerful technique for probing the electronic structure of materials. Here, we report the construction of a tabletop 10.8-eV femtosecond
laser through cascaded third-harmonic generation, which operates at a repetition rate of 1 MHz and delivers a
photon flux of approximately 1012
photons/s. The system achieves a high energy resolution of approximately 11.8 meV and tunable polarization. This flexibility enables detailed studies of orbitaland (pseudo)
spin characteristics in
quantum materials. We demonstrate the capabilities of this
laser-ARPES system by investigating several prototypical materials, showcasing its potential for elucidating complex phenomena in
quantum materials.
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All authors Jisong Gao, Qiaoxiao Zhao, Wenbo Liu, Dong Li, Zhicheng Gao, Yudian Zhou, Xuegao Hu, Zhihao Cai, Zhilin Li, Youguo Shi, Peng Cheng, Zhaojun Liu, Lan Chen, Kehui Wu, Zhigang Zhao, Baojie Feng
Chemical Physics
eT 2.0: An efficient open-source molecular electronic structure program
Sarai Dery Folkestad, Eirik F. Kjønstad, Alexander C. Paul et al. (+34 more)
The eT program is an open-source electronic structure program with emphasis on performance and modularity. As its name suggests, the program features extensive coupled cluster capabilities, performing well compared to other electronic structure programs, and, in some cases, outperforming commercial alternatives. However, eT is more than a coupled cluster program; other models based on wave function theory (such as full and reduced space configuration interaction and a variety of self-consistent field models) and density functional theory are supported. The second major release of the program, eT 2.0, has specialized functionality for strong light-matter coupling conditions. In addition, it includes a wide range of optimizations and algorithmic improvements, as well as new capabilities for exploring potential energy surfaces and for modeling experiments in the UV and X-ray regimes. Molecular gradients are now available at the coupled cluster level, and high-accuracy spectroscopic simulations are available at reduced computational cost within the multilevel coupled cluster and multiscale frameworks. We present the modifications to the program since its first major release, eT 1.0, highlighting some notable new features and demonstrating the performance of the new version relative to the first release and to other established electronic structure programs.
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All authors Sarai Dery Folkestad, Eirik F. Kjønstad, Alexander C. Paul, Rolf H. Myhre, Riccardo Alessandro, Sara Angelico, Alice Balbi, Alberto Barlini, Andrea Bianchi, Chiara Cappelli, Matteo Castagnola, Sonia Coriani, Yassir El Moutaoukal, Tommaso Giovannini, Linda Goletto, Tor S. Haugland, Daniel Hollas, Ida-Marie Høyvik, Marcus T. Lexander, Doroteja Lipovec, Gioia Marrazzini, Torsha Moitra, Ylva Os, Regina Paul, Jacob Pedersen, Matteo Rinaldi, Rosario R. Riso, Sander Roet, Enrico Ronca, Federico Rossi, Bendik S. Sannes, Anna Kristina Schnack-Petersen, Andreas S. Skeidsvoll, Leo Stoll, Guillaume Thiam, Jan Haakon M. Trabski, Henrik Koch
Medical Physics
A GPU-based Monte Carlo framework for IMRT QA using EPID transit dosimetry
Ning Gao, Didi Li, Na Liu et al. (+5 more)
Purpose: We presented a GPU-based MC framework, ARCHER-EPID, specifically designed for EPID transit dosimetry, with improving accuracy and efficiency. Methods: A comprehensive MC framework was developed to perform full radiation transport simulations through three distinct zones: a detailed linear accelerator head model, a CT-based patient/phantom geometry, and a realistic, multi-layered EPID model. To convert the simulated absorbed dose to a realistic detector signal, a dose-response correction model was implemented. The framework was validated by comparing simulations against experimental measurements for 25 IMRT fields delivered to both a solid water phantom and a anthropomorphic phantom. Agreement was quantified using Gamma analysis. Results: The GPU-accelerated ARCHER-EPID framework can complete the simulation for a complex IMRT field in about 90 seconds. A 2D correction factor lookup table is generated by parameterizing radiological thickness and effective field size to account for the EPID's energy-dependent response. The data revealed that for small fields, beam hardening is the dominant effect, while for large fields, the contribution from patient-generated scatter overwhelms this effect. The average 2D gamma passing rates (3%/3 mm criteria) between simulation and measurements are 98.43% for the solid water phantom and 97.86% for the anthropomorphic phantom, respectively. Visual comparison of the images and dose profiles between simulation and measurements show a high degree of agreement. Conclusions: We have successfully developed and validated a GPU-based MC framework that provides gold-standard accuracy for EPID transit dosimetry in radiotherapy. The results demonstrate that our proposed method has potential for routine application in PSQA.
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All authors Ning Gao, Didi Li, Na Liu, Yankui Chang, Qiang Ren, Xi Pei, Zhi Wang, Xie George Xu
Chemical Physics
Atomic and electronic structure of poly-[Ni(Salen)]: combined study by XPS, UV PES, NEXAFS and DFT methods
Petr M. Korusenko, Olga V. Petrova, Anatoliy A. Vereshchagin et al. (+8 more)
A detailed study of poly-[Ni(Salen)]
polymer in its oxidized (Ox) and reduced (Red) states was conducted using X-ray photoelectron (XPS) and ultraviolet photoemission (UV PES) spectroscopy, near-edge X-ray absorption fine structure (NEXAFS) spectroscopy, and
quantum-chemical calculations. XPS analysis revealed significant energy shifts (-1.5 to -1.8 eV) and broadening of the PE lines for all atoms upon polymerization, indicating a major redistribution of valence electron density between the monomer fragments. In the oxidized
polymer, new features in the Ni 2p and O 1s PE spectra were associated with the formation of polarons with weakened Ni-O bonds; this effect diminished upon reduction as the number of polarons decreased.
Quantum-chemical calculations attributed the valence band broadening to enhanced C 2p contributions from $\pi$-conjugation between monomers. NEXAFS spectroscopy confirmed the stability of the ethylenediamine fragment and the direct involvement of the phenolic rings of the salen ligand in polymerization, also revealing a partial weakening and incomplete restoration of the $\pi$ bonding between O and Ni atoms upon reduction. Furthermore, it was shown that it is the $BF_{4}^-$ anions that weaken the Ni-O bonds during oxidation, which are partially preserved in the reduced state.
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All authors Petr M. Korusenko, Olga V. Petrova, Anatoliy A. Vereshchagin, Oleg V. Levin, Ratibor G. Chumakov, Konstantin P. Katin, Sergey V. Nekipelov, Victor N. Sivkov, Alexandra V. Koroleva, Alexander S. Konev, Alexander S. Vinogradov
Fluid Dynamics
Assessment of modern shock capturing schemes for all-speed flows in the OpenFOAM framework
Anurag Adityanarayan Ray, Sreejita Bhaduri, Swetarka Das et al. (+1 more)
OpenFOAM is a widely used computational fluid dynamics (CFD) framework based on the finite volume method for solving a wide range of flow problems. However, its default numerical schemes, particularly the Kurganov-Noelle-Petrova (KNP) method used for shock capturing, are only low-order accurate. This work presents the implementation of modern high-order Riemann solvers along with AUSM+up (Advection Upstream Splitting Method) and LDFSS (Low
Diffusion Flux Splitting Scheme) within the OpenFOAM environment. It evaluates them across test cases of increasing complexity. Results show that the default KNP scheme is robust but overly diffusive on coarse grids, suppressing flow features, while finer grids introduce spurious oscillations. The solver remains stable only under low Courant numbers but can tolerate mild numerical noise at higher values (around 0.5). A Total Variation Diminishing (TVD) Runge-Kutta time integration enhances stability while preserving accuracy. Among the tested flux schemes, HLLC (Harten-Lax-van Leer Contact) and its corrected variants HLLC-LM (Low-Mach correction) and HLLCP (pressure dissipation), as well as AUSM+up and LDFSS, all improve shock and contact-wave resolution on coarse grids. While the standard HLLC suffers from grid-aligned discontinuities, the corrected forms overcome these issues. AUSM+up introduces slightly higher dissipation and underperforms in deep subsonic regimes. In contrast, LDFSS provides comparable accuracy to HLLC-type solvers but is computationally expensive at very low Mach numbers and fails for strong unsteady shocks. The findings guide OpenFOAM users in selecting suitable shock-capturing schemes for specific flow regimes.
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All authors Anurag Adityanarayan Ray, Sreejita Bhaduri, Swetarka Das, Ashoke De
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Variational Calculations of the Excited States of the Charged NV-center in Diamond Using a Hybrid Functional
Lei Sun, Elvar Örn Jónsson, Aleksei Ivanov et al. (+2 more)
The excited electronic states involved in the optical cycle preparation of a pure
spin state of the negatively charged NV-defect in diamond are calculated using the HSE06 hybrid density functional and variational optimization of the orbitals. This includes the energy of the excited triplet as well as the two lowest singlet states with respect to the ground triplet state. In addition to the vertical excitation, the effect of structural relaxation is also estimated using analytical atomic forces. The lowering of the energy in the triplet excited state and the resulting zero-
phonon line triplet excitation energy are both within 0.1 eV of the experimental estimates. An analogous relaxation in the lower energy singlet state using
spin purified atomic forces is estimated to be 0.06 eV. These results, obtained with a hybrid density functional, improve on previously published results using local and semi-local functionals, which are known to underestimate the band gap. The good agreement with experimental estimates demonstrates how time-independent variational calculations of excited states using density functionals can give accurate results and, thereby, provide a powerful screening tool for identifying other defect systems as candidates for
quantum technologies.
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All authors Lei Sun, Elvar Örn Jónsson, Aleksei Ivanov, Ji Chen, Hannes Jónsson
Fluid Dynamics
Local polynomial solutions of steady Euler equations for planar ideal fluids
Wenan Zou
Exploring the general analytical solutions to the Euler equations for ideal fluids holds significant theoretical and practical importance. The steady flows in two-dimensional spaces are considered whether there is an analytical solution in the form of finite
polynomials defined in the local region. By employing the tensorial representation and the complex variable notation, we successfully work out the local analytical solutions in terms of
polynomials with highest degree up to four, and the general form of solutions for higher-degree
polynomials can also be anticipated. Several examples are illustrated and some discussion comments on the effect of the viscous term from the Navier-Stokes (N-S) equations are presented.
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Geophysics
Global Chlorophyll-a Retrieval algorithm from Sentinel 2 Using Residual Deep Learning and Novel Machine Learning Water Classification
Yotam Sherf, Bar Efrati, Gabriel Rozman et al. (+1 more)
We present the Global Water Classifier (GWC), a supervised, geospatially extensive Machine Learning (ML) classifier trained on Sen2Cor corrected Sentinel-2 surface reflectance data. Using nearly 100 globally distributed inland water bodies, GWC distinguishes water across Chlorophyll-a (Chla) levels from non-water spectra (clouds, sun glint, snow, ice, aquatic vegetation, land and sediments) and shows geographically stable performance. Building on this foundation model, we perform Chla
retrieval based on a matchup Sentinel-2 reflectance data with the United States Geological Survey (USGS) AquaMatch in-situ
dataset, covering diverse geographical and hydrological conditions. We train an XGBoost regressor on 13626 matchup points. The positive labeled scenes by the GWC consistently outperform the negatives and produce more accurate Chla
retrieval values, which confirms the classifiers advantage in reducing various interferences. Next, residual analysis of the regression predictions revealed structured errors, motivating a residual CNN (RCNN) correction stage. We add a CNN residual stage trained on normalized residuals, which yield substantial improvement. Our
algorithm was tested on 867 water bodies with over 2,000 predictions and Chla values up to 1000~mg$/m^{3}$, achieving $R^2$ = 0.79, MAE = 13.52~mg$/m^{3}$, and slope = 0.91, demonstrating robust, scalable, and globally transferable performance without additional tuning.
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All authors Yotam Sherf, Bar Efrati, Gabriel Rozman, Moshe Harel
Plasma Physics
Effect of flow-aligned external magnetic fields on mushroom instability
Y. Guo, D. Wu and J. Zhang
Mushroom instability (MI) is a shear instability considered responsible for generating and amplifying
magnetic fields in relativistic jets. While astrophysical jets are usually magnetized, how MI acts in magnetized jets remains poorly understood. In this paper, we investigate the effect of a flow-aligned external
magnetic field on MI, with both theoretical analyses and particle-in-
cell (PIC) simulations. In the limit of a cold and collisionless
plasma, we derive a generalized dispersion relation for linear growth rates of the magnetized MIs. Numerical solutions of the dispersion relation reveal that the external
magnetic field always suppresses the growth of MI, though MIs are much more robust against the external
magnetic field than electron-scale Kelvin-Helmholtz instabilities (ESKHIs). Analyses are also extended to instabilities with an arbitrary wavevector in the shear interface plane, where coupling effect is observed for sub-relativistic scenarios. Two-dimensional PIC simulations of single-mode MIs reach a good agreement with our analytical predictions, and we observe formation of a quasi-steady saturation structure in magnetized runs. In simulations with finite temperatures, we observe the competition and cooperation between MIs and a
diffusion-induced DC
magnetic field.
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Physics and Society
Exploring Emergent Topological Properties in Socio-Economic Networks through Learning Heterogeneity
Chanuka Karavita, Zehua Lyu, Dharshana Kasthurirathna et al. (+1 more)
Understanding how individual learning behavior and structural dynamics interact is essential to modeling emergent phenomena in socioeconomic networks. While bounded rationality and network adaptation have been widely studied, the role of heterogeneous learning rates both at the
agent and network levels remains under explored. This paper introduces a dual-learning framework that integrates individualized learning rates for
agents and a rewiring rate for the network, reflecting real-world cognitive diversity and structural adaptability. Using a simulation model based on the Prisoner's Dilemma and Quantal Response
Equilibrium, we analyze how variations in these learning rates affect the emergence of large-scale network structures. Results show that lower and more homogeneously distributed learning rates promote scale-free networks, while higher or more heterogeneously distributed learning rates lead to the emergence of core-periphery
topologies. Key topological metrics including scale-free exponents, Estrada heterogeneity, and assortativity reveal that both the speed and variability of learning critically shape system rationality and network architecture. This work provides a unified framework for examining how individual learnability and structural adaptability drive the formation of socioeconomic networks with diverse
topologies, offering new insights into adaptive behavior, systemic organization, and resilience.
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All authors Chanuka Karavita, Zehua Lyu, Dharshana Kasthurirathna, Mahendra Piraveenan
Chemical Physics · Cross-listed from quant-ph
Dynamical system analysis of quantum tunneling in an asymmetric double-well potential
Swetamber Das and Arghya Dutta
We study
quantum tunneling in an asymmetric double-well potential using a dynamical systems–based approach rooted in the Ehrenfest formalism. In this framework, the time
evolution of a Gaussian wave packet is governed by a hierarchy of coupled equations linking lower- and higher-order position moments. An approximate closure scheme, required to render the system tractable, yields a reduced dynamical system for the mean and variance, with skewness explicitly entering due to the potential's asymmetry. Stability analysis of this system identifies energy thresholds for detectable tunneling across the barrier and reveals regimes where tunneling, though theoretically allowed, remains practically undetectable. Comparison with full numerical solutions of the time-dependent Schrödinger equation shows that, beyond reproducing key tunneling features, the dynamical systems approach provides an interpretable description of
quantum transport through tunneling in an effective asymmetric two-level system.
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Instrumentation and Detectors
Tritiated methane reduction in the PandaX-4T experiment via purge and cryogenic distillation processes
Shuaijie Li, Zhou Wang, Xiangyi Cui et al. (+7 more)
Tritium from tritiated methane (CH$_3$T) calibration is a significant
impurity that restricts the sensitivity of the PandaX-4T dark matter detection experiment in the low-energy region. The CH$_3$T removal is essential for PandaX-4T and other
liquid xenon dark matter direct detection experiments, as CH$_3$T serves as a critical component for low-energy calibration. To eliminate CH$_3$T, the xenon in the detector is suitably recuperated, leaving 1.8 bar of xenon gas inside, and the detector is flushed with heated xenon gas. Concurrently, leveraging the lower boiling point of methane relative to xenon, the PandaX-4T cryogenic
distillation system is effectively utilized to extract CH$_3$T from xenon after optimizing the operational parameters. Following the commissioning run, 5.7 tons of xenon are purified via the
distillation method. Recent data indicate that the CH$_3$T concentration reduces from $3.6\times10^{-24}$ mol/mol to $5.9\times10^{-25}$ mol/mol, demonstrating that gas purging and
distillation are effective in removing CH$_3$T, even at concentrations on the order of $10^{-24}$ mol/mol.
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All authors Shuaijie Li, Zhou Wang, Xiangyi Cui, Li Zhao, Yonglin Ju, Wenbo Ma, Yingjie Fan, Jianglai Liu, Liqiang Liu, Kai Kang
Computational Physics
Hybrid Neural Interpolation of a Sequence of Wind Flows
Ameir Shaa, Claude Guet, Xiasu Yang et al. (+3 more)
Rapid and accurate urban wind field prediction is essential for modeling particle transport in emergency scenarios. Traditional Computational Fluid Dynamics (CFD) approaches are too slow for real-time applications, necessitating surrogate models. We develop a hybrid neural interpolation method for constructing surrogate models that can update urban wind maps on timescales aligned with meteorological variations. Our approach combines Tucker tensor decomposition with neural networks to interpolate Reynolds-Averaged Navier-Stokes (RANS) solutions across varying inlet wind angles. The method decomposes high-dimensional velocity, pressure, and eddy viscosity field
datasets into a core tensor and factor
matrices, then uses
Fourier interpolation for angular modes and k-nearest neighbors convolution for spatial interpolation. A neural network correction mitigates interpolation artifacts while preserving physical consistency. We validate the approach on a simple cylinder-sphere configuration and, relative to a strong pure neural network
benchmark, achieve comparable or improved accuracy ($R^2 > 0.99$) with significantly reduced training time. The pure NN remains a feasible reference model; the hybrid provides an accelerated approximate alternative that suppresses spurious oscillations, maintains wake dynamics, and demonstrates computational efficiency suitable for real-time urban wind simulation.
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All authors Ameir Shaa, Claude Guet, Xiasu Yang, Armand Albergel, Bruno Ribstein, Maxime Nibart
Chemical Physics
Benchmarking foundation potentials against quantum chemistry methods for predicting molecular redox potentials
Yicheng Chen, Lixue Cheng, Yan Jing et al. (+1 more)
Computational high-throughput virtual screening is essential for identifying redox-active molecules for sustainable applications such as electrochemical carbon capture. A primary challenge in this approach is the high computational cost associated with accurate
quantum chemistry calculations. Machine learning foundation potentials (FPs) trained on extensive density functional theory (DFT) calculations offer a computationally efficient alternative. Here, we
benchmark the MACE-OMol-0 and UMA FPs against a hierarchy of DFT functionals for predicting experimental molecular redox potentials for both electron transfer (ET) and
proton-coupled electron transfer (PCET) reactions. We find that these FPs achieve exceptional accuracy for PCET processes, rivaling their target DFT method. However, the performance is diminished for ET reactions, particularly for multi-electron transfers involving reactive ions that are underrepresented in the OMol25 training data, revealing a key out-of-distribution limitation. To overcome this, we propose an optimal hybrid workflow that uses the FPs for efficient geometry optimization and thermochemical analysis, followed by a crucial single-point DFT energy refinement and an implicit solvation correction. This pragmatic approach provides a robust and scalable strategy for accelerating high-throughput virtual screening in sustainable chemistry.
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All authors Yicheng Chen, Lixue Cheng, Yan Jing, Peichen Zhong
History and Philosophy of Physics
Algorithmic Randomness, Exchangeability, and the Principal Principle
Jeffrey A. Barrett and Eddy Keming Chen
We introduce a framework uniting algorithmic randomness with exchangeable credences to address foundational questions in philosophy of probability and philosophy of science. To demonstrate its power, we show how one might use the framework to derive the Principal Principle – the norm that rational credence should match known objective chance – without circularity. The derivation brings together de Finetti's exchangeability, Martin-Löf randomness, Lewis's and Skyrms's chance-credence norms, and statistical constraining laws (arXiv:2303.01411). Laws that constrain histories to algorithmically random sequences naturally pair with exchangeable credences encoding inductive
symmetries. Using the de Finetti representation
theorem, we show that this pairing directly entails the Principal Principle of this framework. We extend the proof to partial exchangeability and provide finite-history bounds that vanish in the infinite limit. The Principal Principle thus emerges as a mathematical consequence of the
alignment between nomological constraints and inductive learning. This reveals how algorithmic randomness and exchangeability can illuminate foundational questions about chance, frequency, and rational belief.
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Computational Physics · Cross-listed from quant-ph
Exploiting biased noise in variational quantum models
Connor van Rossum, Sally Shrapnel and Riddhi Gupta
Variational
quantum algorithms (VQAs) are promising tools for demonstrating
quantum utility on near-term
quantum hardware, with applications in optimisation,
quantum simulation, and machine learning. While researchers have studied how easy VQAs are to train, the effect of
quantum noise on the classical optimisation process is still not well understood. Contrary to expectations, we find that twirling, which is commonly used in standard error-mitigation strategies to symmetrise noise, actually degrades performance in the variational setting, whereas preserving biased or non-unital noise can help classical optimisers find better solutions. Analytically, we study a universal
quantum regression model and demonstrate that relatively uniform Pauli channels suppress gradient magnitudes and reduce expressivity, making optimisation more difficult. Conversely, asymmetric noise such as amplitude damping or biased Pauli channels introduces directional bias that can be exploited during optimisation. Numerical experiments on a variational eigensolver for the transverse-field Ising model confirm that non-unital noise yields lower-energy states compared to twirled noise. Finally, we show that coherent errors are fully mitigated by re-parameterisation. These findings challenge conventional noise-mitigation strategies and suggest that preserving noise biases may enhance VQA performance.
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Physics and Society
Graph conductance, synchronization, and a new local bottleneck measure for decentralized network optimization
C. Tyler Diggans, Jeremie Fish and Abd AlRahman R. AlMomani
The two most prominent bottleneck measures in graph theory, commonly known as the isoperimetric number and conductance, are both referred to in the literature as Cheeger constants. While these measures are useful for assessing barriers to flow in networked systems, neither is sufficient to characterize the stability of complete synchronization, i.e. the dynamic convergence of every node in the system toward a synchronization
manifold. Conductance, in particular, does provide an effective bound on the coupling strength required to achieve bulk synchronization, but it often fails to differentiate between many chimera states. The Fiedler vector, which is the eigenvector associated with the algebraic connectivity of the graph, is similarly often able to correctly identify the limiting cut, however, it can fail as well being a global relaxation, especially in the presence of small-set bottlenecks. Furthermore, the computations of these properties are NP-Hard and require global information about the network structure, which is often unrealistic for many complex systems. We define a normalized synchronization bottleneck ratio for each bi-partitioning graph cut. The minimum of this ratio over all such cuts gives the synchronization bottleneck measure as a global network property, and the associated cut better identifies the true limiting bottleneck for complete synchrony for a specific class of network coupled dynamics. Obtaining this global minimizer is also NP-hard, but due to the use of only local information in the argument, heuristics based on the ratio itself can guide decentralized strategies for improving the synchronizability in man-made networked systems; having relevance in power grid stability, distributed database coherence, and other applications where the
alignment of every node is required for the proper functioning of the system.
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Atomic Physics
Fast Penning Ionization of Cold Rydberg atoms in an Electric Field
Changjie Luo, Feng Fang, Wenchang Zhou et al. (+3 more)
We observe a fast Penning ionization in a dilute gas of cold rubidium Rydberg atoms, in the presence of a static electric field of 50 V/cm, with the ionization rate coefficients for two specific states being measured, which are orders of magnitude higher than the theoretical predictions in field-free space. Our analysis based on a polarized two-atom model reveals that the ionization threshold of Rydberg atoms is lowered by the static electric field, reducing the energy exchange required for Penning ionization and increasing the ionization rate. Beyond this, the dipole-dipole interaction strengthened by the electric field between two Rydberg atoms at a micrometer-scale distance leads to double ionization of the atoms pair, opening a new autoionization channel. Such enhancement of the Penning ionization by a static electric field poses both a threat to the stability and a potential control strategy for
quantum systems composed of cold Rydberg atoms with micrometer-scale interatomic separations.
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All authors Changjie Luo, Feng Fang, Wenchang Zhou, Peng Zhang, Xinwen Ma, Jie Yang
Fluid Dynamics
The Geometry of Contraction-Induced Flows
Aaron Winn and Eleni Katifori
Peristalsis is the driving mechanism behind a broad array of biological and engineered flows. In peristaltic pumping, a wave-like contraction of the tube wall produces local changes in volume which induce flow. Net flow arises due to
geometric nonlinearities in the momentum equation, which must be properly captured to compute the flow accurately. While most previous models focus on radius-imposed peristalsis, they often neglect longitudinal length changes - a natural consequence of radial contraction in elastic materials. In this paper, to capture a more accurate picture of peristaltic pumping, we calculate the flow in an elastic vessel undergoing contractions in the transverse and longitudinal directions simultaneously, keeping the
geometric nonlinearities arising in the strain. A careful analysis requires us to study our fluid using the Lagrangian coordinates of the elastic tube. We perform analytic calculations of the flow characteristics by studying the fluid inside a fixed boundary with time-dependent metric. This mathematical manipulation works even for large-amplitude contractions, as we confirm by comparing our analytical results to COMSOL simulations. We demonstrate that transverse and longitudinal contractions induce instantaneous flows at the same order in wall strain, but in opposite directions. We investigate the influence of the wall's Poisson ratio on the flow profile. Incompressible walls suppress flow by minimizing local volume changes, whereas auxetic walls enhance flow. For radius-imposed peristaltic waves, wall incompressibility reduces both reflux and particle trapping. In contrast, length-imposed waves typically generate backflow, although trapping can still occur at large amplitudes for some Poisson ratios. These results yield a more complete description of peristalsis in elastic media and offer a framework for studying contraction-induced flows more broadly.
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Biological Physics · Cross-listed from q-bio.QM
Machine learning approaches for interpretable antibody property prediction using structural data
Kevin Michalewicz, Mauricio Barahona and Barbara Bravi
Understanding the relationship between antibody sequence, structure and function is essential for the design of antibody-based therapeutics and research tools. Recently, machine learning (ML) models mostly based on the application of large
language models to sequence information have been developed to predict antibody properties. Yet there are open directions to incorporate structural information, not only to enhance prediction but also to offer insights into the underlying molecular mechanisms. This chapter provides an overview of these approaches and describes two ML frameworks that integrate structural data (via graph representations) with neural networks to predict properties of antibodies: ANTIPASTI predicts binding affinity (a global property) whereas INFUSSE predicts residue flexibility (a local property). We survey the principles underpinning these models; the ways in which they encode structural knowledge; and the strategies that can be used to extract biologically relevant statistical signals that can help discover and disentangle molecular determinants of the properties of interest.
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Physics and Society
Scale invariance and statistical significance in complex weighted networks
Filipi N. Silva, Sadamori Kojaku, Alessandro Flammini et al. (+2 more)
Most networks encountered in nature, society, and technology have weighted edges, representing the strength of the interaction/association between their vertices. Randomizing the structure of a network is a classic procedure used to estimate the statistical significance of properties of the network, such as transitivity, centrality and community structure. Randomization of weighted networks has traditionally been done via the weighted configuration model (WCM), a simple extension of the configuration model, where weights are interpreted as bundles of edges. It has previously been shown that the ensemble of randomizations provided by the WCM is affected by the specific scale used to compute the weights, but the consequences for statistical significance were unclear. Here we find that statistical significance based on the WCM is scale-dependent, whereas in most cases results should be independent of the choice of the scale. More generally, we find that designing a null model that does not violate scale invariance is challenging. A two-step approach, originally introduced for network reconstruction, in which one first randomizes the structure, then the weights, with a suitable distribution, restores scale invariance, and allows us to conduct unbiased assessments of significance on weighted networks.
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All authors Filipi N. Silva, Sadamori Kojaku, Alessandro Flammini, Filippo Radicchi, Santo Fortunato
Instrumentation and Detectors
Exploring an image-based $b$-jet tagging method using convolution neural networks
Hangil Jang and Sanghoon Lim
Jet
flavor tagging, the identification of jets originating from $c$-
quarks, $b$-
quarks, and other
quarks (light
quarks and
gluons), is a crucial task in high-energy heavy-ion physics, as it enables the investigation of
flavor-dependent responses within the hot and dense
nuclear medium produced in heavy-ion collisions. Recently, several methods based on deep learning techniques, such as deep neural networks and graph neural networks, have been developed. These deep-learning-based methods demonstrate significantly improved performance compared to traditional methods that rely on track impact parameters and secondary vertices. In the tagging
algorithms, various properties of jets and constituent charged particles are used as input parameters. We explore a new method based on images surrounding the primary vertex, utilizing charged particles within the jet cone, which can be measured using a silicon tracking system. For this initial experimental study, we assume the ideal performance of the tracking system. To analyze these images, we employed convolutional neural networks. The image-based
flavor tagging method shows an 80-90% $b$-jet tagging efficiency for jets in the transverse momentum range from 20 to 100 GeV/$c$. This approach has the potential to significantly improve the accuracy of jet
flavor tagging in high-energy
nuclear physics experiments.
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