Computational Physics · Cross-listed from quant-ph
Memory-enhanced quantum extreme learning machines for characterizing non-Markovian dynamics
Hajar Assil, Abderrahim El Allati and Gian Luca Giorgi
We use a
Quantum Extreme Learning Machine for characterizing and estimating parameters of
quantum dynamics generated by a tunable collision model. The input to the learning protocol consists of
quantum states produced by successive system environment interactions, while the reservoir is implemented as a disordered many body
quantum system evolving under a fixed
Hamiltonian. We systematically explore how extending the QELM feature space, through the inclusion of temporal information and additional observables, affects estimation performance. Our results demonstrate that temporal extensions of the feature vector consistently and significantly enhance estimation accuracy relative to the baseline protocol. Notably, incorporating
memory from earlier time steps yields the most substantial and robust improvements, whereas extensions based solely on additional observables offer only marginal gains. Crucially, the advantage conferred by temporal
memory becomes increasingly pronounced as the dynamics become more strongly non Markovian, indicating that environmental
memory effects serve as a constructive resource for learning.
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Geophysics · Cross-listed from cs.LG
Domain-informed explainable boosting machines for trustworthy lateral spread predictions
Cheng-Hsi Hsiao, Krishna Kumar and Ellen M. Rathje
Explainable Boosting Machines (EBMs) provide transparent predictions through additive shape functions, enabling direct inspection of feature contributions. However, EBMs can learn non-physical relationships that reduce their reliability in natural hazard applications. This study presents a domain-informed framework to improve the physical consistency of EBMs for lateral spreading prediction. Our approach modifies learned shape functions based on domain knowledge. These modifications correct non-physical behavior while maintaining data-driven patterns. We apply the method to the 2011 Christchurch earthquake
dataset and correct non-physical trends observed in the original EBM. The resulting model produces more physically consistent global and local explanations, with an acceptable tradeoff in accuracy (4–5%).
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Optics
Experimental Scaling of Diffraction Efficiency in Laser-Induced Plasma Gratings
M. M. Wang, V. M. Perez-Ramirez, N. M. Fasano et al. (+8 more)
We demonstrate efficient diffraction of intense ultrashort
laser pulses using optical-field-ionization-induced
plasma-neutral gratings formed by spatially structured ionization of a neutral molecular gas in the interference field of two femtosecond pump pulses. The transient refractive index modulation of the
plasma structure persists for at least 10 picoseconds and is used to diffract intense femtosecond signal pulses into the 1st order of diffraction with an average efficiency of up to 35$\%$.
Plasma gratings are shown to provide stable diffraction at signal
laser intensities greater than $ 10^{14}\text{ W/cm}^2$, exceeding the damage thresholds of conventional solid-state optics by more than two orders of magnitude, continuously for hours at a 10-Hz repetition rate. The experimental diffraction efficiency scales with the grating aperture allowing for a larger millimeter-scale
plasma optic, increases with the pump energy and electron density, and reaches a maximum at a specific grating length in agreement with the coupled-mode theory for periodic media. These results demonstrate the scalability, tunability, and high damage threshold of transmissive
plasma-based photonic structures, opening new prospects for controlling multi-petawatt
laser beams.
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All authors M. M. Wang, V. M. Perez-Ramirez, N. M. Fasano, K. Ou, S. Cao, V. Dewan, A. M. Giakas, A. Morozov, P. Michel, M. R. Edwards, J. M. Mikhailova
Optics · Cross-listed from eess.IV
A Lensless Polarization Camera
Noa Kraicer, Shay Elmalem, Erez Yosef et al. (+2 more)
Polarization imaging is a technique that creates a pixel map of the polarization state in a scene. Although invisible to the human eye, polarization can assist various sensing and computer
vision tasks. Existing polarization cameras use spatial or temporal multiplexing, which increases the camera volume, weight, cost, or all of the above. Recent lensless imaging approaches, such as DiffuserCam, have demonstrated that compact imaging systems can be realized by replacing the lens with a coding element and performing computational reconstruction. In this work, we propose a compact lensless polarization camera composed of a diffuser and a simple striped polarization mask. By combining this optical design with a reconstruction
algorithm that explicitly models the polarization-encoded lensless measurements, four linear polarization images are recovered from a single snapshot. Our results demonstrate the potential of lensless approaches for polarization imaging and reveal the physical factors that govern reconstruction quality, guiding the development of high-quality practical systems.
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All authors Noa Kraicer, Shay Elmalem, Erez Yosef, Hani Barhum, Raja Giryes
Applied Physics · Cross-listed from quant-ph
Die to wafer direct bonding of (100) single-crystal diamond thin films for quantum optoelectronics
Dominic Lepage, Amin Yaghoobi, Heidi Tremblay et al. (+1 more)
This work unlocks the manufacturing of nanophotonic
quantum systems that exploit the unique material properties of single-
crystal diamond (SCD). We achieve this by introducing a
semiconductor-compatible process for the direct bonding of multiple high-quality, ultrathin diamond films onto a carrier wafer, enabling the subsequent parallel nanofabrication of optoelectronic integrated
circuits. Central to this approach is a new diamond surface-preparation method that avoids boiling tri-acid mixtures while producing exceptionally clean 20 um thin single
crystals. These platelets are bonded side-by-side to 100 mm silica wafers and exhibit a record shear strength of 45.1 MPa for (100)-oriented diamond, surpassing all previously reported bonding attempts. Evidence indicates that the bonding is dominated by van der Waals interactions, likely arising from mismatched protonation mechanisms between Si-OH and C-OH surface terminations, rather than from covalent-bond-driven mechanisms. Despite this non-molecular nature, the
heterostructures remain stable through
liquid immersions and standard nanofabrication steps. Because the method depends primarily on surface cleanliness and roughness rather than specific chemistries, it is broadly transferable across wafer materials. This capability to parallel-bond ultrathin SCD films onto large-area substrates provides a scalable route to high-performance platforms spanning nanophotonic
quantum technologies, high-power electronics, MEMS, and biotechnology.
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All authors Dominic Lepage, Amin Yaghoobi, Heidi Tremblay, Dominique Drouin
Data Analysis and Statistics · Cross-listed from astro-ph.EP
Long-term outburst activity of comet 17P/Holmes and constraints on ejecta size distributions
Maria Gritsevich, Marcin Wesołowski, Josep M. Trigo-Rodríguez et al. (+4 more)
A quantitative understanding of cometary outbursts requires robust constraints on the size distribution of ejected particles, which governs outburst dynamics and underpins estimates of released gas and
dust. In the absence of direct measurements of particle sizes, assumptions about the size distribution play a central role in modelling
dust-trail formation, their dynamical
evolution and observability, and the potential production of meteor showers following encounters with Earth. We analyse brightness amplitude variations associated with outbursts of comet 17P/Holmes from 1892 to 2021, with particular emphasis on the exceptional 2007 mega-outburst. During this event the comet underwent a rapid and substantial brightening: at its peak, the expanding coma reached a diameter exceeding that of the Sun and briefly became the largest object in the
Solar System visible to the naked eye. We constrain the size distribution and total mass of porous agglomerates composed of ice, organics, and
dust ejected during the outburst. The inferred particle size distribution is consistent with a power law of index q, yielding effective particle sizes between 10^-6 m for q = 4 and 5 x 10^-3 m for q = 2. Accounting for effective particle size, sublimation flux, and bulk density, we find that the total number of ejected particles increases with both q and sublimation flux. These results place constraints on the physical properties of outburst ejecta and provide physically motivated initial conditions for long-term
dust-trail
evolution modelling. They further indicate that cometary outburst brightness is determined primarily by the number of particles and their size distribution, rather than by the total ejected mass alone, with direct implications for the origin and
evolution of meteoroid streams and the interplanetary
dust population.
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All authors Maria Gritsevich, Marcin Wesołowski, Josep M. Trigo-Rodríguez, Alberto J. Castro-Tirado, Jorma Ryske, Markku Nissinen, Peter Carson
Plasma Physics
Pair-loaded electron-only magnetic reconnection using laser-driven capacitor coils
Brandon K. Russell, Qian Qian, Rebecca Fitzgarrald et al. (+12 more)
We propose and simulate a laboratory platform to study the effects of positrons in
magnetic reconnection using
laser-driven capacitor coils. Using particle-in-
cell simulations, we show that externally injected MeV electron-positron pairs are trapped in the coil current sheet, significantly modifying the reconnection dynamics and particle acceleration. These pairs increase the reconnection rate by a factor of approximately 8, which Ohm's law decomposition reveals to be driven by the divergence of the generalized pressure tensor. Based on their high energy and magnetization, the pairs also substantially broaden the
diffusion region. Particle tracking simulations in realistic coil
magnetic fields further demonstrate that injected pairs can remain confined for several picoseconds, providing conditions for sustained interaction with the reconnection region. These results establish a near-term pathway to laboratory studies of positron-influenced reconnection, bridging high-energy-density experiments with pair-dominated astrophysical environments.
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All authors Brandon K. Russell, Qian Qian, Rebecca Fitzgarrald, Yang Zhang, Stepan S. Bulanov, Sergei V. Bulanov, Hui Chen, Lan Gao, Gabriele M. Grittani, Xiaocan Li, Kian Orr, Geoffrey Pomraning, Kevin M. Schoeffler, Alexander G. R. Thomas, Hantao Ji
Optics
High-Capacity Urban Terrestrial Free-Space Optical Communication Links at km-Scale
Vincent van Vliet, Menno van den Hout, Eduward Tangdiongga et al. (+1 more)
Free-space optical communication links can enable high-capacity wireless connectivity in urban areas. We discuss the feasibility, challenges, and recent developments for high-capacity urban free-space optical links at kilometer scale.
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All authors Vincent van Vliet, Menno van den Hout, Eduward Tangdiongga, Chigo Okonkwo
Atomic Physics
From MOT to BEC using a single crossed-wire pair
Joshua M. Wilson, James A. Stickney, Francisco Fonta et al. (+4 more)
We demonstrate a new magneto-optical trap (MOT) configuration using a simple pair of crossed wires rotated at 45 deg and an appropriate bias field to generate a MOT of >10^8 atoms. The same pair of wires, with slightly adjusted control parameters, is then used to magnetically trap the atoms and cool them via forced evaporative cooling into a Bose-Einstein condensate (BEC) with >10^4 atoms. We present the theoretical framework for generating a quadrupole field using a pair of crossed wires with arbitrary rotation angle, along with the atom chip design and fabrication. Finally, we describe the experimental protocols required for BEC production using only a single crossed-wire atom chip.
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All authors Joshua M. Wilson, James A. Stickney, Francisco Fonta, Johnathan White, Brian Kasch, Spencer E. Olson, Matthew B. Squires
Atmospheric and Oceanic Physics
Significant Wave Height Estimation Incorporating Second-Order Scattering
Senal Chandrasekara and Reza Shahidi
Traditional significant wave height (SWH) estima- tion from HF radar typically relies on spectral analysis of the received radar signals. This process was previously simplified by establishing a linear relationship between SWH and the standard deviation of received HF radar voltages under first- order scattering. Building on this approach, this paper presents a physics-informed regression model that incorporates second- order scattering effects through a quadratic formulation derived from a Neumann expansion. The proposed method is evaluated using HF radar data collected in July 2018 at Argentia, New- foundland, with collocated buoy measurements as ground truth. The model achieves a minimum root-mean-square error (RMSE) of approximately 19 cm.
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Optics · Cross-listed from quant-ph
Quantum reservoir computing with classical and nonclassical states in an integrated optical circuit
S. Świerczewski, W. Verstraelen, P. Deuar et al. (+3 more)
Quantum reservoir computing (QRC) is a hardware-implementation-friendly
quantum neural network scheme with minimal physical system requirements and a proven advantage over classical counterparts. We use an extension of the positive-P phase space method to efficiently simulate a bosonic, linear silicon-chip based QRC system excited with a single nonclassical state, a "kitten" state. In combination with input-encoding coherent states, our method allows to obtain exact results for all correlation functions without
Hilbert space cutoff. Surprisingly, we find that such a setting - where the only "quantumness” derives from a single input mode, is sufficient to obtain significant (over 9-fold) reduction of classification error over the classical counterpart. Our work provides a promising direction toward efficient
quantum computation with accessible optical hardware.
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All authors S. Świerczewski, W. Verstraelen, P. Deuar, T. C. H. Liew, A. Opala, M. Matuszewski
Applied Physics · Cross-listed from cond-mat.supr-con
Interplay of superconductivity and ferromagnetism in ferromagnetic semiconductor-based Josephson junctions
Hirotaka Hara, Lukas Baker, Axel Leblanc et al. (+8 more)
The interplay between superconductivity and ferromagnetism has long been pursued as a route to unconventional Josephson effects, yet suitable material platforms remain limited. Here we report Josephson junctions based on epitaxial Al/InAs/(Ga,Fe)Sb
heterostructures grown by low-temperature molecular beam epitaxy, achieving atomically abrupt
superconductor/
semiconductor/
ferromagnetic interfaces. The devices exhibit clear proximity-induced superconductivity, including multiple Andreev reflections and gate-tunable supercurrents, confirming transparent coupling across the hybrid structure. Under perpendicular
magnetic fields, the junctions reveal highly unconventional Fraunhofer interference patterns with hysteresis, flux jumps, asymmetric lobe
evolution, and clear nonreciprocity, providing strong evidence of induced ferromagnetism and broken time-reversal
symmetry in the superconducting channel. Gate control further modulates the critical current, highlighting the semiconducting nature of the system. Our results demonstrate that
ferromagnetic semiconductor heterostructures can serve as a highly tunable platform for exploring proximity-induced superconductivity and superconducting diode effects, and for advancing device concepts at the intersection of magnetism and
quantum electronics.
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All authors Hirotaka Hara, Lukas Baker, Axel Leblanc, Shingen Miura, Keita Ishihara, Melissa Mikalsen, Patrick J. Strohbeen, Jacob Issokson, Masaaki Tanaka, Javad Shabani, Le Duc Anh
Atomic Physics · Cross-listed from quant-ph
Full-quantum variational dynamics simulation for time-dependent Hamiltonians with global spectral discretization
Minchen Qiao, Zi-Ming Li and Yu-xi Liu
The most widely used approach for simulating the dynamics of time-dependent
Hamiltonians via
quantum computation depends on the
quantum-classical hybrid variational
quantum time
evolution algorithm, in which ordinary differential equations of the variational coefficients for determining time
evolution are solved via classical simulations with a time discretization method. We here present a full-
quantum approach, in which ordinary differential equations of the variational coefficients are transformed into static linear equations via the Chebyshev spectral discretization method and then solved via the
quantum singular value transformation
algorithm. Our full
quantum algorithm avoids classical feedback, achieves exponential convergence for smooth
Hamiltonians, and yields a
quantum circuit depth that is independent of the number of time steps. We demonstrate two implementation strategies, with a global formulation designed for fault-tolerant architectures and a sequential formulation tailored to near-term devices, and validate the approach through numerical simulations of
proton-hydrogen charge-transfer dynamics, a prototypical time-dependent
quantum chemistry problem. This work establishes a systematic pathway from
quantum-classical hybrid variational
quantum algorithms to full-
quantum solvers for general time-dependent
Hamiltonians, particularly those whose dynamics admit compact variational descriptions, opening a route toward full
quantum computational advantages in time-dependent simulations.
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Fluid Dynamics
Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization
Isaac Robledo, Alberto Vilariño, Arnau Miró et al. (+3 more)
Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-consistent aerodynamic metrics. The framework couples a low-fidelity-informed Gaussian process regression transfer model with uncertainty-triggered sampling and a synchronized elitism rule embedded in a hybrid genetic
algorithm. Low-fidelity XFOIL evaluations provide inexpensive features, while sparse RANS simulations are adaptively allocated when predictive uncertainty exceeds a threshold; elite candidates are mandatorily validated at high fidelity, and the population is re-evaluated to prevent evolutionary selection based on outdated fitness values produced by earlier surrogate states. The method is demonstrated for a two-point problem at $Re=6\times10^6$ with cruise at $\alpha=2^\circ$ (maximize $E=L/D$) and take-off at $\alpha=10^\circ$ (maximize $C_L$) using a 12-parameter CST representation. Independent multi-fidelity surrogates per flight condition enable decoupled refinement. The optimized design improves cruise efficiency by 41.05% and take-off lift by 20.75% relative to the best first-generation individual. Over the full campaign, RANS evaluations were required for only 14.78% and 9.5% of the condition-specific candidate evaluations at cruise and take-off, respectively. These percentages quantify the reduction in high-fidelity usage relative to the fixed automated RANS workflow adopted as the high-fidelity reference in this study.
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All authors Isaac Robledo, Alberto Vilariño, Arnau Miró, Oriol Lehmkuhl, Carlos Sanmiguel Vila, Rodrigo Castellanos
Optics
Attractor-Keyed Memory
Natalia G. Berloff
Physical selectors (
lasers choosing a mode, Ising machines settling on a ground state, condensates occupying a
spin state) produce high-dimensional signatures at the moment of decision: full field amplitudes, multimode interference patterns, or scattering responses. These signatures are richer than the winner's index, yet they are routinely discarded. We show that when the signatures are repeatable across trials (stereotyped) and linearly independent across routes, a single linear decoder compiled from calibration data maps them to arbitrary payloads, merging selection and
memory access into one event and eliminating the fetch that dominates latency and energy in sparse routing architectures. The construction requires one SVD of measured device responses, which certifies capability and bounds worst-case error for any downstream payload before the task is chosen. Runtime error separates into two independently diagnosable channels, decoding fidelity (controlled by dictionary conditioning $\sigma_{\min}(\Phi)$) and routing reliability (controlled by the margin-to-noise ratio $\Delta/T_{\mathrm{eff}}$), each with a distinct physical origin and targeted remedy. We derive the full error decomposition, give Ising-machine selector constructions, and validate the predicted scalings on synthetic speckle-signature simulations across three measurement modalities. No hardware demonstration exists; we provide a falsifiable four-step experimental protocol specifying what a first experiment must measure. Whether real device signatures satisfy stereotypy is the central open question.
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Optics
Ultra-Thin Aluminum-Doped Silver for Transmissive Thermally Reconfigurable Visible Photonics
Hongyi Sun, Yi-Siou Huang, Junyeob Song et al. (+7 more)
Functional materials with high electrical conductivity and optical transmittance are vital for thermally tunable free-space photonic systems. Conventional transparent conductors such as
graphene and indium tin
oxide are limited by high contact resistance, poor mechanical stability, or complex fabrication. Ultra-thin metals, such as pure silver, have also been explored with limited success due to thermal instability and dewetting. Here, we propose an ultra-thin Al-doped Ag film to tackle these challenges. Aluminum promotes heterogeneous nucleation of silver, enabling the formation of continuous, smooth films that are thermally stable at reduced thicknesses while maintaining excellent electrical conductivity and transparency. We find that a 12 nm Al-doped Ag film exhibits an average transmittance of 80% across the visible range with a sheet resistance of 8.3$\pm$1.16 $\Omega$cm$^2$. Moreover, on-chip Al-doped Ag microheaters exhibit uniform, rapid thermal response, and stable electrical performance, maintaining functionality for over $10^7$ ON and OFF cycles at temperatures below 400$\deg$C. Furthermore, as a
benchmark, we demonstrate reversible phase-change switching in Ge$_2$Sb$_2$Se$_4$Te (GSST) and VO$_2$. 30$\times$30 $\mu$m$^2$ GSST
cells exhibited complete crystallization and amorphization under 2.2 V - 200 ms and 4.1V - 50$\mu$s pulses, respectively, resulting in a 40% transmission contrast at 780 nm and a tenfold improvement in power consumption compared to similar devices. Additionally, VO$_2$ films displayed reversible
insulator-to-metal transitions near 65{\deg}C with reflectance and transmittance modulation in the visible and the near-infrared at frequencies up to 25 Hz with room for improvement. These results establish Al-doped Ag as a robust transparent metallic heater for integration in dynamic metasurfaces, optical coatings, and more.
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All authors Hongyi Sun, Yi-Siou Huang, Junyeob Song, Francis Vásquez-Aza, Christopher S. Whittington, Nathan Youngblood, Sharon M. Weiss, Georges Pavlidis, Amit Agrawal, Carlos A Rios Ocampo
Computational Physics · Cross-listed from cond-mat.stat-mech
Rejection-free Glauber Monte Carlo for the 2D Random Field Ising Model via Hierarchical Probabilistic Counters
Luca Cattaneo, Federico Ettori, Giovanni Cerri et al. (+2 more)
We present an efficient Monte Carlo
algorithm for the simulation of the two-dimensional Random Field Ising Model (RFIM). The method combines the event-driven, rejection-free character of the Bortz Kalos-Lebowitz (BKL)
algorithm with Glauber transition probabilities, introducing hierarchical probabilistic counters to perform
spin selection in O(log N) operations. This enables efficient sampling of the system's dynamics, especially in the low-temperature and low-disorder regime, where traditional Metropolis updates suffer from critical slowing down. Furthermore, this approach allows a proper dynamical simulation of the Ising system's behavior even in the presence of a Random Field (RF), unlike the BKL method. RFIM simulations with Gaussian field distributions reproduce the expected reduction of the pseudo-critical temperature with increasing disorder. Benchmarking shows speedups exceeding two orders of magnitude compared to the Metropolis
algorithm in the low-temperature regime. The proposed method provides an efficient and dynamically faithful tool for studying both
equilibrium and non-
equilibrium phenomena in disordered
spin systems.
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All authors Luca Cattaneo, Federico Ettori, Giovanni Cerri, Paolo Biscari, Ezio Puppin
Instrumentation and Detectors · Cross-listed from cond-mat.mtrl-sci
Accelerating Structure-Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy
Jiawei Gong, Danqing Ma, Ralph Bulanadi et al. (+4 more)
Microscopy combined with local spectroscopy is widely used to correlate nanoscale structure with functional properties in materials, but conventional measurements rely heavily on human-selected sampling locations and predefined targets, limiting
dataset diversity and the potential for discovery. Here, we present a framework that integrates autonomous microscopy with a dual-novelty deep kernel learning (DN-DKL) for adaptive data acquisition and a dual variational autoencoder (VAE) for representation learning. DN-DKL actively guides the microscopy toward structurally and spectroscopically novel regions, enabling efficient collection of large spectral
datasets. Dual-VAE embeds local structure and spectroscopic responses into a shared latent
manifold that serves as a structure-property relationship map. We applied this framework for the investigation of halide perovskite films using conductive atomic force microscopy. The results reveal distinct hysteresis behaviors that are linked to specific nanoscale structural motifs, including grain boundary junction points that show hysteresis under different bias conditions and asymmetric grain boundaries that suppress charge transport. This framework establishes a general strategy that leverages the complementary strengths of self-driving microscopy, machine learning, and human expertise to accelerate scientific discovery in functional materials.
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All authors Jiawei Gong, Danqing Ma, Ralph Bulanadi, Robert Moore, Rama Vasudevan, Lianfeng Zhao, Yongtao Liu
Applied Physics · Cross-listed from cond-mat.mes-hall
Maxim Trushin
Field emission from metals underpinned early
vacuum-tube technology, and recent nanoscale engineering made field-emission devices compatible with modern silicon platforms. However, the limited tunability of electron transport in metals has restricted their applicability. Here, we show that noble metals coated with
graphene exhibit clean non-monotonic $I-V$ characteristics arising from resonant tunneling through
graphene's electronic states, enabled by
graphene's atomic thinness and weak electronic hybridization with noble metals. Our approach combines ab-initio interface parameters with exact solutions of the Schrödinger equation for electron transmission across the interface. We analyze two experimentally relevant geometries: a vertical configuration with a flat suspended emitter and a coplanar configuration with sharp electrodes allowing for strong field enhancement and gating. These results establish a practical route to tunable electron transport in metal
heterostructures, positioning them as competitive components for air-channel field-emission nanoelectronics.
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Chemical Physics
Extended Lagrangian molecular dynamics on vibronic surfaces in the nuclear-electronic orbital framework
Joseph A. Dickinson, Mathew Chow, Eno Paenurk et al. (+1 more)
Proton transfer is central to many processes of chemical interest. The simulation of
proton transfer dynamics requires the inclusion of
nuclear quantum effects, such as zero-point energy,
nuclear delocalization, and tunneling. Herein, we introduce methods within the
nuclear-electronic orbital (NEO) framework, where specified
nuclei are treated
quantum mechanically on the same level as the electrons, for the simulation of
proton transfer dynamics. Specifically, NEO density functional theory is used to treat the transferring
protons quantum mechanically, and the other
nuclei are propagated classically on the adiabatic vibronic ground-state surface. We formulate a NEO extended Lagrangian molecular dynamics (NEO-ELMD) approach to incorporate the motion of the
nuclear basis function centers during such simulations. Density
matrix extrapolation and purification are introduced as a means to accelerate the NEO self-consistent field procedure at each time step by reducing the number of iterations required for convergence. We demonstrate the fidelity and efficiency of NEO-ELMD by comparison to related dynamics methods for intramolecular
proton transfer in malonaldehyde. We also use these accelerated techniques to simulate the nonequilibrium single and double
proton transfer dynamics of
proton-coupled electron transfer in much larger benzimidazole-phenol systems. This work provides a foundation for future methodologies to efficiently simulate
proton transfer dynamics within the NEO-DFT framework while incorporating nonadiabatic effects between adiabatic vibronic states.
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All authors Joseph A. Dickinson, Mathew Chow, Eno Paenurk, Sharon Hammes-Schiffer
Chemical Physics
Comment on "Efficient implementation of the superposition of atomic potentials initial guess for electronic structure calculations in Gaussian basis sets"
Kshitijkumar A. Surjuse, Zhihao Deng, Andrey Asadchev et al. (+1 more)
In J. Chem. Phys. 152, 144105 (2020) Lehtola et al introduced the efficient Gaussian-basis representation of Superposition of Atomic Potentials (SAP) which "can be easily implemented in any Gaussian-basis
quantum chemistry code in terms of two-electron integrals". Here we demonstrate that it is possible to evaluate Gaussian AO representation of SAP by nearly trivial modification of one-electron
nuclear attraction integrals.
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All authors Kshitijkumar A. Surjuse, Zhihao Deng, Andrey Asadchev, Edward F. Valeev
Biological Physics · Cross-listed from cs.LG
Stochastic Resetting Accelerates Reinforcement Learning Beyond Random Search
Jello Zhou, David J. Schwab and Vudtiwat Ngampruetikorn
Stochastic resetting – intermittently returning a process to a fixed reference state – has emerged as an effective mechanism for optimizing first-passage properties. Existing theory largely treats processes that search but do not learn: the searcher follows fixed dynamics, accumulating no knowledge between resets. Here we ask how
stochastic resetting interacts with reinforcement learning, where the underlying dynamics adapt through experience. In tabular grid environments, we find that resetting can accelerate learning even when it does not reduce the search time of a diffusive
agent. Our results reveal a distinct additional mechanism through which resetting speeds the propagation of reward information. We show that deterministic, sharp resetting accelerates learning more than the
stochastic protocol but over a narrower range of reset rates. In a continuous-state task with neural-network-based value approximation, we demonstrate that resetting speeds up learning when exploration is hard and rewards are sparse. We argue further that, in the tabular tasks, resetting accelerates learning without altering the solution the
agent ultimately reaches, unlike other techniques such as temporal discounting, which biases the optimal behavior. Our results establish
stochastic resetting as a simple, tunable mechanism for accelerating learning by shaping how experience accumulates, extending a canonical phenomenon of statistical mechanics to adaptive systems.
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Computational Physics
Sub-cell Wave Reconstruction from Differentiated Riemann Variables
Steve Shkoller
We introduce a postprocessing procedure that recovers sub-
cell wave geometry from a standard one-dimensional Euler shock-capturing computation using differentiated Riemann variables (DRVs) – characteristic derivatives that separate the three wave families into distinct localized spikes. Filtered DRV surrogates detect the waves, plateau sampling extracts the local states, and a pressure-wave-function Newton closure completes the geometry. The entire pipeline adds less than $0.25\%$ to the cost of a baseline WENO–5/HLLC solve. For Sod, a severe-expansion problem, and the LeBlanc shock tube, wave locations are recovered to within roundoff or $O(10^{-4})$ and the contact is sharpened to one
cell width; a pattern-agnostic extension handles all four Riemann configurations with errors at the $10^{-6}$–$10^{-8}$ level. Direct comparison with MUSCL–THINC–BVD and WENO-Z–THINC–BVD shows that neither reproduces the combination of sharp contacts, small contact-window internal-energy error, and elimination of the LeBlanc positive overshoot achieved by the DRV reconstruction.
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Optics
Correlations of the phase gradients of the light wave propagating in a turbulent medium in the regime of strong scintillations
V.A.Bogachev, I.V. Kolokolov, V.V. Lebedev et al. (+2 more)
We investigate analytically and numerically correlation functions of the phase of light waves that propagate through turbulent media. We examine the case of strong scintillations that occur at large values of the Rytov dispersion, $\sigma^2_R$. Then, it is possible to relate the pair correlation function of phase gradients to the known pair correlation function of the envelope dependent on the distance $r$ assuming Gaussianity of the envelope of the beam. Our direct numerical simulations show that the profile of the pair correlation function for phase gradients gradually approaches the theoretical expression as the value of $\sigma_R^2$ increases, if $r<r_0$ where $r_0$ is the Fried length. For larger $r$ the behavior of the computed correlation function is quite different because of destroying the Gaussianity.
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All authors V.A.Bogachev, I.V. Kolokolov, V.V. Lebedev, A.V.Nemtseva, F.A.Starikov
Optics · Cross-listed from nlin.PS
Impact of phase modulation on the dynamics of temporal localized structures in injected Kerr microcavities
Marc Hunkemöller, Thomas G. Seidel, Julien Javaloyes et al. (+1 more)
We theoretically investigate how phase modulation alters the dynamics of temporal localized structures (TLSs) in vertically emitting Kerr micro-cavities under detuned optical injection operating in the normal dispersion regime. We show that the emergence of TLSs in general is governed by a synchronization between the imposed modulation and the intrinsic pulse dynamics. We perform a multi-parameter bifurcation analysis of the underlying delay-algebraic equation model in the uniform field limit and demonstrate that weakly nonlinear and dissipative Hermite-Gauss modes shape the dynamics of dark TLSs, leading to a complex hybrid bifurcation structure. Beyond the uniform field limit, both bright and dark modulated TLSs are shown to exist and to occupy distinct
equilibrium positions within the cavity. An effective equation of motion for the TLS positions is derived, showing a good agreement with the full model.
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All authors Marc Hunkemöller, Thomas G. Seidel, Julien Javaloyes, Svetlana V. Gurevich
Instrumentation and Detectors
Investigating Ultra-Low Energy Ionization Yield from Nuclear Recoils in Semiconductor Detectors via Molecular Dynamics Simulations
Chang-Hao Fang
Nuclear recoil ionization yield constitutes a critical uncertainty source in low-energy detection for dark matter (DM) and coherent elastic
neutrino-
nucleus scattering (CE$\nu$NS) experiments. We present a novel methodology employing molecular dynamics simulations to assess ionization yields in crystalline
semiconductor detectors. This non-parameterized approach resolving inherent limitations of traditional Lindhard model through explicit incorporation of
crystal condensed matter effects, facilitating a seamless reliability from high-energy ($E>10$\,keV) to electron-hole pair (EHP) regimes. Our model achieves the best agreement with experimental data in silicon to date, especially at the minimal energy level of a single EHP. Meticulously consideration of ion transport mechanisms reveals fundamental ionization yield distributions, superseding conventional single-value models. The distributional paradigm extends the DM-nucleon elastic scattering exclusion limit to 0.29\,GeV/$c^2$ under single-EHP sensitivity. We further report advancements in modeling
quantum effects and channeling phenomena affecting ionization yields in high-purity germanium detectors.
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Optics
Efficient generation of entangled photons in the telecommunications range using nonlinear metasurfaces integrated with ScAlN/GaN heterostructures
Jaeyeon Yu, Jewel Mohajan, Mikhail Tokman et al. (+8 more)
Entangled
photons provide non-classical correlations that enable measurement sensitivities beyond classical limits, scalable fault-tolerant
quantum computation, and fundamentally secure
quantum communication, making them a foundational necessity for next-generation
quantum technologies. Here we propose and analyze a novel source of entangled
photons based on ScAlN/GaN
quantum wells integrated with dielectric metasurfaces.
Giant second-order intersubband nonlinearity of the GaN
quantum wells with strain-compensated delta-doped ScAlN barriers caused by strong built-in electric fields combined with superior mode-coupling performance of metasurfaces optimized by inverse design give rise to efficient parametric down-conversion and generation of entangled
photons in the telecom range. We develop a rigorous Heisenberg-Langevin formalism which includes field quantization, dissipation and fluctuations for all fields, parametric amplification of thermal noise and zero-point fluctuations, and other relevant effects. Our proposed approach of employing the emergent photonic material ScAlN promises high biphoton generation rate over $10^{10}$ s$^{-1}$ from a compact integrated structure that is only 0.5 $\mu$m thick while mitigating strain-related issues that have so far impeded progress of nitride-based
heterostructures for
quantum photonic applications into the infrared and visible wavelengths. Our result therefore is relevant for numerous applications ranging from
quantum sensing,
quantum information, and computing.
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All authors Jaeyeon Yu, Jewel Mohajan, Mikhail Tokman, Jackson Stewart, Anthony Rice, Sadhvikas Addamane, Oana Malis, Alejandro W. Rodriguez, Igal Brener, Raktim Sarma, Alexey Belyanin
Physics and Society
Assessment of Latent Pedestrian-Vehicle Interaction Risk Profiles at Midblock Crossing in VR
Rulla Al-Haideri, Bilal Farooq and Elisabetta Cherchi
Pedestrian safety at midblock crossings is a critical concern in mixed traffic environments where autonomous vehicles (AVs) and human-driven vehicles (HDVs) share the road. Pedestrians often infer intent from vehicle motion in AV encounters, making them vulnerable to small shifts in conflict margins. This study investigates whether virtual reality (VR) crossing sessions separate into distinct interaction risk profiles and whether AV-only sessions shift profile prevalence compared to HDV-only sessions. Using large-scale immersive VR experiments from Toronto, Canada, and Newcastle, England, we compute surrogate safety measures (SSMs) and apply latent profile analysis (LPA) to identify distinct pedestrian crossing stances, ranging from risk-accepting to highly cautious. Key findings show that Newcastle exhibits a higher prevalence of high-urgency risk profiles in AV-only sessions, indicating that AVs contribute to higher-risk encounters. In contrast, Toronto shows no significant difference between AV-only and HDV-only sessions, suggesting that contextual factors influence the impact of AVs on pedestrian safety.
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Optics
Designing a low-loss high reflectivity mirror for gravitational waves detectors by combining a dielectric metasurface and a multilayer stack
Edith Hartmann, Michel Lequime, Jerome Degallaix et al. (+4 more)
The design of low-mechanical-loss, high reflectivity mirrors is crucial in the development of the next generation of gravitational-wave observatories. Currently, the amorphous multilayer reflective coatings which are deposited at the surface of the test masses in interferometric gravitational-wave detectors present a major limiting factor in detector sensitivity due to their thermal noise. These coatings require a large number of thin layers to achieve ultra-high reflectivity. However, the thermal noise generated by this type of stack increases with the number of layers used. These dielectric mirrors represent a very mature technology, with current research producing only incremental improvements, highlighting the need for new technical solutions that can address this specific issue. Here, we provide insights into the expected performance of mirrors that combine a resonant metasurface with a multilayer stack. The suggested mirror design ensures the high reflectivity required for interferometric gravitational wave detectors, while using fewer layers of properly selected materials. As a result, it significantly reduces the total coating thickness, making it a promising option for reducing thermal noise as well.
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All authors Edith Hartmann, Michel Lequime, Jerome Degallaix, Michael T. Hartman, Paul Rouquette, Claude Amra, Myriam Zerrad
Applied Physics
Experimental Limit on Neutron Orbital Angular Momentum Detection Using Polarized 3He
D. Sarenac, O. Lailey, D. V. Garrad et al. (+11 more)
A recent proposal suggested that
neutron orbital angular momentum (OAM) states could be detected via
spin-polarized absorption in polarized 3He, with predicted cross-section variations linked to the
neutron's OAM. We experimentally tested this hypothesis using
spin-polarized
neutron beams with OAM =-2 to 2, generated by fork-dislocation phase-gratings, and transmitted through a polarized 3He
cell. Within statistical precision, no OAM-dependent change in the absorption cross section was observed. This null result places stringent constraints on polarized 3He-based OAM detection schemes. The absence of an effect in the given regime is traced to the proposal's disregard of the spatial character of
neutron OAM: unlike
spin, OAM arises from the transverse phase structure of the wavefunction and couples only through spatial gradients and overlap. The transverse extent of
neutron OAM modes expands rapidly, producing a doughnut-shaped intensity profile with negligible overlap with on-axis 3He
nuclei, while off-axis capture samples only a locally uniform phase and reduces the interaction to the known
spin dependence. These results clarify the limits of absorptive
nuclear methods for probing
neutron OAM and emphasize the necessity of spatially resolved interactions in any viable detection scheme.
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All authors D. Sarenac, O. Lailey, D. V. Garrad, P. R. Vadnere, N. Shentevski, C. W. Clark, D. G. Cory, J. P. Cotter, H. Ekinci, M. G. Huber, J. W. Paster, Y. Tzeng, D. Alba Venero, D. A. Pushin