Showing 1–50 of 340 results for author: Guo, X

A compact low-frequency optomechanical triaxial inertial sensor

Authors: Daniel George, Jose D. Hernandez Rivero, Moritz Mehmet, Ramses Miranda Espino, Xiangyu Guo, Andrea Nelson, Jose Sanjuan, Felipe Guzman

Abstract: Triaxial optomechanical accelerometers offer compact, ground-testable alternatives to electrostatic sensors for satellite geodesy, seismometry, and various other applications. We demonstrate a low-CSWaP triaxial sensor using monolithic fused-silica resonators with dual heterodyne interferometric readout. The X axis reaches a 60~pico-g/$\sqrt{\mathrm{Hz}}$ readout noise floor, and the in-plane axes… ▽ More Triaxial optomechanical accelerometers offer compact, ground-testable alternatives to electrostatic sensors for satellite geodesy, seismometry, and various other applications. We demonstrate a low-CSWaP triaxial sensor using monolithic fused-silica resonators with dual heterodyne interferometric readout. The X axis reaches a 60~pico-g/$\sqrt{\mathrm{Hz}}$ readout noise floor, and the in-plane axes resolve ambient seismic ground motion in agreement with a co-located commercial seismometer from 4~mHz to 8~Hz. The Z axis exhibits higher noise under $1g$ loading due to gravity-induced geometric stiffening (44.45~Hz versus 11.07~Hz in a 0g-equivalent configuration), with measurements indicating in-orbit performance on par with the in-plane sensors. These results support the feasibility of triaxial optomechanical accelerometry for space missions and ground-based applications. △ Less

Submitted 18 August, 2026; originally announced August 2026.

Comments: 14 pages, 7 figures

Focal-point scanning for dose delivery and optimization with focused laser-accelerated very-high-energy electron beams

Authors: Zhiyuan Guo, Yifei Pi, Junwei Zhou, Guoqing Liu, Wenbo Zhang, Haiyang Wang, Yaping Qi, Xiaoming Guo, Yuhan Zhang, Bo Peng, Jianfei Hua, Yang Wan, Wei Lu

Abstract: Focused very-high-energy electron (VHEE) beams can produce localized dose enhancement at selected depths, but irradiation of a finite target requires coordinated control of multiple focal positions, incidence directions, and beam weights while limiting exposure of nearby organs at risk (OARs). We present Focal-Point Scanning (FPS), a dose delivery and optimization method developed for laser wakefi… ▽ More Focused very-high-energy electron (VHEE) beams can produce localized dose enhancement at selected depths, but irradiation of a finite target requires coordinated control of multiple focal positions, incidence directions, and beam weights while limiting exposure of nearby organs at risk (OARs). We present Focal-Point Scanning (FPS), a dose delivery and optimization method developed for laser wakefield accelerator (LWFA)-driven VHEE beams. The method is based on a two-dipole focusing system that produces single-plane beam convergence and allows the focal position to be varied by changing the magnetic field strength. FPS distributes focal points throughout the planning target volume and determines focal-point-specific incidence sectors according to the geometry of nearby critical OARs. The method was evaluated using the AAPM TG119 C-shape benchmark and one previously treated lung radiotherapy case. At matched target coverage, FPS reduced the TG119 Core mean dose by approximately one half relative to parallel VHEE and intensity-modulated x-ray plans, approaching the single-field proton pencil-beam-scanning reference. In the lung case, FPS maintained target coverage comparable to the clinical volumetric modulated arc therapy reference while reducing the mean dose to every evaluated OAR; spinal-cord mean and maximum doses decreased by 93.2% and 87.2%, respectively. The evaluated OAR mean doses varied little across rms energy spreads of 0 to 10% and for a flat-top electron spectrum spanning 150 to 250 MeV. These results demonstrate that focal-point-specific angular selection can translate focused-beam physics into effective OAR sparing and support FPS as a planning strategy for broadband LWFA-VHEE radiotherapy. △ Less

Submitted 13 August, 2026; originally announced August 2026.

Comments: 31 pages, 18 figures

Heterogeneously Integrated Squeezed-Light Generation and Detection on a Single Photonic Chip

Authors: Haoran Chen, Benjamin Westcott, Fatemehsadat Tabatabaei, Xiangwen Guo, Shuman Sun, Zijiao Yang, Gedalia Y. Koehler, Beichen Wang, Shadrach Sarpong, Steven Bowers, Olivier Pfister, Andreas Beling, Xu Yi

Abstract: Squeezed light underpins quantum-enhanced sensing and continuous-variable quantum information processing, and integrated photonics offers a route to producing it at scale. Universal to these applications are squeezed-light generation and measurement. Importantly, quantum measurements serve not only as readout but also as active operations in quantum-state evolution. However, integrating squeezed-l… ▽ More Squeezed light underpins quantum-enhanced sensing and continuous-variable quantum information processing, and integrated photonics offers a route to producing it at scale. Universal to these applications are squeezed-light generation and measurement. Importantly, quantum measurements serve not only as readout but also as active operations in quantum-state evolution. However, integrating squeezed-light generation and photodetection on the same photonic chip has remained challenging because they impose fundamentally conflicting material requirements: low optical loss to preserve quantum correlations, but efficient photon absorption for photodetection. Here, we demonstrate squeezed-light generation, routing, and balanced homodyne detection integrated on a single photonic chip through heterogeneous integration. A two-mode squeezed quantum microcomb comprising 34 quantum modes is measured with approximately 3 dB squeezing. Our work establishes a scalable architecture for fully integrated squeezed-light quantum photonic systems, unifying quantum-state generation, processing, and detection on a single chip. △ Less

Submitted 13 August, 2026; originally announced August 2026.

Physics-informed genetic algorithms (PIGAs) facilitating LIBS spectral normalization with shockwave characteristics

Authors: Ying Zhou, Jian Wu, Mingxin Shi, Minxin Chen, Jinghui Li, Xinyu Guo, Yuhua Hang, Cuixiang Pei, Xingwen Li

Abstract: Inspired by physics-informed neural networks (PINNs) inheriting both the interpretability of physical laws and the efficient integration capability of machine learning, we propose a framework based on stoichiometric ablation for LIBS spectral normalization, encoding physical constraints between LIBS intensities and shockwave characteristics (temperature Tshock and pressure P) into optimization alg… ▽ More Inspired by physics-informed neural networks (PINNs) inheriting both the interpretability of physical laws and the efficient integration capability of machine learning, we propose a framework based on stoichiometric ablation for LIBS spectral normalization, encoding physical constraints between LIBS intensities and shockwave characteristics (temperature Tshock and pressure P) into optimization algorithms with multiple independent objectives, named physics-informed genetic algorithms (PIGAs). It is characterized by its applicability to the wider laser energy range covering laser-induced breakdown to significant plasma shielding and spectral lines undergoing self-absorption outperforming the widely-used physical linear or multivariate data-driven normalization methods. The home-made end-to-end LAP-RTE codes serves as the benchmark to validate the physical reciprocal-logarithmic transformation and its extensibility to self-absorption spectral lines for PIGAs. Next experimental spectral lines are statistically used to validate PIGAs correction effects, the median RSDs of spectral intensities can be effectively reduced by 85% (corrected by P) and 88% (corrected by Tshock) for 108 Fe I lines, while for 33 Fe II lines, reduced by 77% (corrected by P) and 86% (corrected by Tshock). Seventeen self-absorption lines are also corrected effectively, with RSDs being reduced by 78% (corrected by P) and 89% (corrected by Tshock). Our proposed idea of combining optimization methods to quantify unknown parameters in normalization strategies can also be extended to excavate the correlation between parameters for other low-temperature plasma fields with similar processes. △ Less

Submitted 24 July, 2026; originally announced August 2026.

Comments: Published in Applied Physics Letters. This is the authors' version. 6 pages, 5 figures

Journal ref: Y. Zhou, J. Wu, M. Shi, M. Chen, J. Li, X. Guo, Y. Hang, C. Pei, and X. Li, Appl. Phys. Lett. 126 (3), 034103 (2025)

Measurement of multiple mechanical properties from multi-dimensional signals in nanosecond laser ablation via PINN

Authors: Ying Zhou, Jian Wu, Ziyuan Song, Jinghui Li, Xinyu Guo, Hao Sun, Yuhua Hang, Cuixiang Pei, Xingwen Li

Abstract: Accurate evaluation of mechanical properties in steels under ageing or service conditions remains a major challenge. We propose a thermo-mechanical coupling framework for nanosecond laser ablation based on energy conservation, which is embedded into a physics-informed neural network (PINN) to enable simultaneous inversion of multiple mechanical properties. A thermo-mechanical coupling coefficient… ▽ More Accurate evaluation of mechanical properties in steels under ageing or service conditions remains a major challenge. We propose a thermo-mechanical coupling framework for nanosecond laser ablation based on energy conservation, which is embedded into a physics-informed neural network (PINN) to enable simultaneous inversion of multiple mechanical properties. A thermo-mechanical coupling coefficient is defined to uniformly describe the dynamic allocation of input laser energy among thermal diffusion, mechanical work and plasma shielding across different deformation stages under laser irradiation. Furthermore, hard-to-measure physical characteristics in the coupled equation are replaced with experimentally accessible features obtained through the simultaneous acquisition of spectroscopic, shockwave and surface-wave signals. Using 210 experimental datasets, the framework simultaneously recovers Young's modulus, yield strength, ultimate tensile strength and micro-Vickers hardness with high accuracy (R2=0.9927, 0.9912, 0.9916 and 0.9959 respectively), significantly outperforming the baseline method (ultrasonic velocity regression for E, R2=0.0012). Comparisons with linear normalization and unconstrained neural networks demonstrate that PINN achieves near-unity accuracy through the embedding of conservation-law constraints. Partial dependency analysis further uncovers the nonlinear coupling laws between input features and mechanical properties. The proposed paradigm, integrating conservation laws, measurable features and physics-informed learning, offers a universal approach for non-contact, high-precision and physically consistent multi-to-multi inversion of multiple material properties under nanosecond laser ablation conditions. △ Less

Submitted 29 July, 2026; originally announced July 2026.

Comments: Published in Applied Physics Letters. This is the authors' version. 6 pages, 5 figures

Journal ref: Y. Zhou, J. Wu, Z.Y. Song, J.H. Li, X.Y. Guo, H. Sun, Y.H. Hang, C.X. Pei, and X.W. Li, Appl. Phys. Lett. 127 (24), 244101 (2025)

PIML-OFEM: A New Large-Scale Structural Analysis Method Based on Problem-Independent Machine Learning and Overlapping Finite Element Technique

Authors: Yilin Guo, Chang Liu, Zongliang Du, Jin Liu, Jingyu Feng, Xinyang Zhang, Yang Li, Tianxing Yang, Changyu Shen, Xu Guo

Abstract: High-resolution analysis and design of large-scale heterogeneous structures require accurate reduced-order models and efficient online computation. Existing multiscale methods must repeatedly construct local basis functions for different material distributions, whereas substructure-based problem-independent machine learning (PIML) methods can be limited by prescribed boundary displacement interpol… ▽ More High-resolution analysis and design of large-scale heterogeneous structures require accurate reduced-order models and efficient online computation. Existing multiscale methods must repeatedly construct local basis functions for different material distributions, whereas substructure-based problem-independent machine learning (PIML) methods can be limited by prescribed boundary displacement interpolation. We propose PIML-OFEM, an overlapping finite element method accelerated by problem-independent machine learning. Each substructure retains only its corner-node degrees of freedom. Oversampled numerical basis functions are constructed by solving local elasticity problems on extended domains and restricting the solutions to the target substructure, eliminating prescribed displacement interpolation on its boundary. Independently constructed local bases are blended through a partition-of-unity overlapping formulation to obtain a globally continuous displacement field. A U-Net learns the mapping from local Young's modulus distributions to numerical basis functions, replacing repeated online local solves and allowing the model to be reused across load cases and global boundary conditions. Numerical examples show close agreement with fine-scale finite element results in displacement and elemental strain energy. PIML-OFEM reduces online computational cost relative to direct finite element analysis and improves accuracy over PIML substructure models based on linear boundary interpolation. In topology optimization, the method supports stable high-resolution iterations with small filter radii and preserves fine-scale features, including local patterns resembling rank-2 microstructures. The framework provides an efficient physics-data approach for large-scale heterogeneous structural analysis and high-resolution topology optimization. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Comments: Submitted to Acta Mechanica Sinica

MSC Class: 74S05; 74P15; 68T07

Spatial nonlocality imaging via metasurface

Authors: Jian Li, Zi-Mu Fan, Qing-Yuan Wu, Wen-Kai Yu, Zhe Meng, Xing-Yan Fan, Wen-Hao Wang, Jie Ma, Xia Guo, An-Ning Zhang

Abstract: Bell nonlocality is both a defining signature of entanglement and a key quantum information resource. However, visualizing and certifying nonlocal correlations across a spatially multimode photonic field remains challenging due to the rapidly growing measurement cost of spatially resolved projective tests. To address this issue, we build a spatial nonlocality imaging scheme that directly reveals t… ▽ More Bell nonlocality is both a defining signature of entanglement and a key quantum information resource. However, visualizing and certifying nonlocal correlations across a spatially multimode photonic field remains challenging due to the rapidly growing measurement cost of spatially resolved projective tests. To address this issue, we build a spatial nonlocality imaging scheme that directly reveals the spatial distribution of quantum nonlocality by integrating a metasurface that performs parallel polarization projections with a quantum-adaptive neural network. Spatially resolved Clauser--Horne--Shimony--Holt (CHSH) tests are realized over a 400-pixel biphoton field using an average of only 1.7 detected coincidence pairs per pixel per basis. This approach yields a nonlocality image that maps the two-dimensional spatial distribution of Bell violations across the optical field and reveals the target-state-dependent spatial evolution of Bell violations. It provides a highly resource-efficient route to large-scale Bell certification and opens new possibilities for exploiting spatially multimode entanglement in quantum imaging, quantum networking, and scalable photonic quantum technologies. △ Less

Submitted 20 July, 2026; originally announced July 2026.

Comments: 18 pages, 5 figures, with 21 pages of Supplementary Materials. Comments are welcome; please contact the corresponding authors for inquiries

Heterogeneously Integrated Balanced Photodetector on an Ultra-Low Loss Silicon Nitride Delay Line Interferometer

Authors: Rahul Chawlani, Fatemehsadat Tabatabaei, Mark W. Harrington, Kaikai Liu, Steven M. Zhu, Jiawei Wang, Meiting Song, Xiangwen Guo, Andreas Beling, Daniel J. Blumenthal

Abstract: Thin core silicon nitride photonics enables ultra-low loss, CMOS foundry compatible integration that supports wavelengths from the visible to shortwave infrared. Applications that can benefit from the resulting lower cost, improved robustness, and portability include quantum sensing and computing, ultra-low noise microwave generation, optical clocks, optical gyros, coherent fiber communications, a… ▽ More Thin core silicon nitride photonics enables ultra-low loss, CMOS foundry compatible integration that supports wavelengths from the visible to shortwave infrared. Applications that can benefit from the resulting lower cost, improved robustness, and portability include quantum sensing and computing, ultra-low noise microwave generation, optical clocks, optical gyros, coherent fiber communications, and fiber sensing. An important next step is integration of functional circuits and systems on chip with heterogeneous integration of active components such as high-performance photodetection. Yet to date integrated high-performance photodetectors on the thin film silicon nitride platform has remained elusive. In this work, we demonstrate heterogeneous integration of an InGaAs on InP substrate Modified Uni-Traveling Carrier balanced photodetector with a 15-meter-long unbalanced thin core silicon nitride Mach-Zehnder Interferometer with a bandwidth of 0.92 GHz and a responsivity of 0.305 A/W at 1550 nm with a propagation loss as low as 2.5 dB/m at 1600 nm. Using this circuit we demonstrate two functions, a meter-scale differential interferometer laser stabilization circuit achieving a nearly 23 dB noise suppression at 1 kHz offset and an optical frequency discriminator frequency noise measurement with high sensitivity across 6 orders of magnitude from 10 Hz to 10 MHz. These results demonstrate that the high performance of thin core silicon nitride devices can be combined with integrated high-performance photodetection to realize on-chip stabilized lasers and circuits and pave the path towards full systems on chip. △ Less

Submitted 3 July, 2026; originally announced July 2026.

Predicting Novel Stable Materials for Experimental Synthesis

Authors: Yuqi An, Sihong Zhu, Joseph Montoya, Xingyu Guo, Zhenbin Wang

Abstract: Machine-learning-accelerated materials discovery has yielded large numbers of computationally stable compounds, yet many remain experimentally unrealized, underscoring a persistent gap between prediction and synthesis. Here, we introduce a hierarchical screening framework that combines PBE-based thermodynamic stability, efficient dynamical-stability screening enabled by universal machine-learning… ▽ More Machine-learning-accelerated materials discovery has yielded large numbers of computationally stable compounds, yet many remain experimentally unrealized, underscoring a persistent gap between prediction and synthesis. Here, we introduce a hierarchical screening framework that combines PBE-based thermodynamic stability, efficient dynamical-stability screening enabled by universal machine-learning interatomic potentials, and SCAN-based thermodynamic refinement. Applying this protocol to the 894 stable materials previously reported in Sci. Data 9, 302 (2022), we first curate 603 unique structures, of which only 298 remain thermodynamically stable on the complete PBE phase diagrams, demonstrating the critical role of competing phases in stability assessment. Dynamical screening then identifies 166 materials stable under both harmonic-phonon and finite-temperature molecular dynamics criteria, and SCAN phase diagrams further narrow this set to 109. Finally, by combining decomposition enthalpy with chemical-space completeness, we prioritize 25 candidates as high-confidence targets for experimental synthesis. This work provides a practical protocol for translating stability predictions into experimentally actionable synthesis targets, closing a key gap in machine-learning-driven materials discovery. △ Less

Submitted 2 July, 2026; originally announced July 2026.

Comments: 28 pages, 7 figures

A Mapping Sheath with Thermally Drawn Multi-Electrode Basket for Cardiac Electrophysiological Recording and Ablation Catheter Delivery

Authors: Qindong Zheng, Anil Demircali, Jinshi Zhao, Xiaotong Guo, Libaihe Tian, Oliver Jones, Jamie Kay, Shengzhe Li, Alex Ranne, Elaine Lim, Huiyi Wu, Simos Koutsoftidis, Mohamed Abdelaziz, Emmanuel Drakakis, Prapa Kanagaratnam, Nick Linton, Burak Temelkuran

Abstract: Cardiac arrhythmias, particularly atrial fibrillation, represent a major cardiovascular health burden and underscore the need for efficient and integrated strategies for electrical mapping and targeted therapy. Cardiac electrophysiology procedures depend on accurate identification of arrhythmogenic substrates followed by timely catheter ablation, but conventional diagnostic and therapeutic devices… ▽ More Cardiac arrhythmias, particularly atrial fibrillation, represent a major cardiovascular health burden and underscore the need for efficient and integrated strategies for electrical mapping and targeted therapy. Cardiac electrophysiology procedures depend on accurate identification of arrhythmogenic substrates followed by timely catheter ablation, but conventional diagnostic and therapeutic devices remain separate, often requiring repeated catheter exchanges and multiple access routes. Here, we report an adaptable strategy for functionalizing hollow-core sheaths with EP mapping capabilities, integrating multielectrode recording and ablation catheter delivery within a single compact platform. The device leverages thermal drawing to enable complex geometric fabrication, miniaturization, rapid prototyping, and scalable manufacturing of ultrathin electrode splines arranged circumferentially at the distal end to form an adjustable basket. The mapping sheath exhibited mechanical and electrophysiological properties suitable for intracardiac navigation and electrogram recording in bench-top evaluations, an in vitro left atrial phantom study, and ex vivo Langendorff-perfused porcine heart testing. In vivo porcine studies further demonstrated translational feasibility through vascular introduction, fluoroscopic visualization, intracardiac deployment, tissue contact, electrogram acquisition, and reconstruction of voltage and activation maps. These results support the development of intracardiac platforms with an adapted manufacturing approach, potentially guiding advances in agile cardiac mapping and ablation. △ Less

Submitted 28 June, 2026; originally announced June 2026.

A GPGPU-Oriented Full Phase-Space Parallel Unified Gas-Kinetic Scheme with Velocity-Block Pipelining

Authors: Zhiwen Zhuang, Yixiao Wang, Xinhang Guo, Xing Ji, Xian Wang, Kun Xu

Abstract: The deterministic unified gas-kinetic scheme (UGKS) provides a multiscale framework for nonequilibrium gas dynamics, but its high-dimensional phase-space discretization leads to severe memory pressure and communication overhead, especially on large unstructured meshes. This paper presents a GPGPU-oriented UGKS with velocity-block pipelining and full phase-space MPI decomposition. In the proposed f… ▽ More The deterministic unified gas-kinetic scheme (UGKS) provides a multiscale framework for nonequilibrium gas dynamics, but its high-dimensional phase-space discretization leads to severe memory pressure and communication overhead, especially on large unstructured meshes. This paper presents a GPGPU-oriented UGKS with velocity-block pipelining and full phase-space MPI decomposition. In the proposed formulation, the discrete velocity space is partitioned into fixed-size velocity blocks for accelerator execution, while MPI ranks are organized into coupled physical-space and velocity-space communicators. As a result, each rank stores and advances only a local physical subdomain together with a contiguous subset of velocity blocks, and macroscopic moments are recovered through lightweight reductions over the velocity-space communicator. To improve concurrency and reduce exposed communication cost, a triple-buffered pipeline is further developed to overlap microscopic reconstruction, physical-halo exchange, nonequilibrium flux evaluation, and the first-stage distribution update during the local velocity-block sweep. The implementation targets SIMT-based GPGPU accelerators through a portable device-runtime abstraction. Numerical experiments demonstrate that the $P_v=8$ configuration achieves a $33.4$--$35.4\times$ strong-scaling speedup on 64 nodes, while an Orion-like capsule simulation reaches approximately $1.33\times10^{11}$ phase-space degrees of freedom on 4096 GPGPU accelerators. These results indicate that the proposed method preserves the original UGKS flux construction and two-stage time discretization, while substantially reducing microscopic storage per rank and improving the scalability of large unstructured phase-space simulations. △ Less

Submitted 14 June, 2026; originally announced June 2026.

Plasma Instabilities in Arbitrary Distributions: Comparison between ALPS and BO

Authors: Xudong Guo, Huasheng Xie, Kristopher G. Klein, D. Verscharen, Chen Shi, Jinsong Zhao

Abstract: Determining accurate wave dispersion relations is a central problem in plasma physics. Recent advances have enabled the numerical computation of linear dispersion relation in plasmas with arbitrary particle velocity distribution functions (VDFs), using two distinct solvers, BO and ALPS. Their reliability and mutual consistency, however, have not been systematically tested for a broad range of VDFs… ▽ More Determining accurate wave dispersion relations is a central problem in plasma physics. Recent advances have enabled the numerical computation of linear dispersion relation in plasmas with arbitrary particle velocity distribution functions (VDFs), using two distinct solvers, BO and ALPS. Their reliability and mutual consistency, however, have not been systematically tested for a broad range of VDFs. Here we compare the dispersion relations obtained from BO and ALPS for several representative distributions. We find that the two solvers give consistent unstable modes for kappa distributions with large values of $κ$, as well as for ring-beam, shell, and proton core-beam distributions. BO, however, becomes unreliable for kappa distributions with $κ< 4$. For an observationally derived VDF, the two solvers give similar real frequencies for the unstable waves but substantially different growth rates. This difference is mainly caused by the imperfect fitting of the input distribution required by BO. Despite this limitation, BO has a clear computational advantage because it can obtain all roots in a single run. Considering the complementary strengths of the two solvers, their combined use can provide a more reliable and effective framework for investigating instabilities in non-Maxwellian plasma environments. △ Less

Submitted 12 June, 2026; originally announced June 2026.

Comments: 11 pages, 9 figures

Integrated magnonic neural circuits based on nonlinear wave neurons

Authors: Mengying Guo, Xudong Jing, Kristýna Davidkova, Roman Verba, Zhenyu Zhou, Xueyu Guo, Carsten Dubs, Chuan Gao, Yiheng Rao, Kaiming Cai, Jing Li, Philipp Pirro, Andrii V. Chumak, Qi Wang

Abstract: Artificial intelligence is driving intense interest in alternative computing hardware capable of neural information processing beyond conventional charge-based electronics. Among emerging approaches, wave-based computing promises highly parallel and energy-efficient operation, but scalable physical neural hardware has remained elusive because wave systems generally lack cascadable nonlinear neuron… ▽ More Artificial intelligence is driving intense interest in alternative computing hardware capable of neural information processing beyond conventional charge-based electronics. Among emerging approaches, wave-based computing promises highly parallel and energy-efficient operation, but scalable physical neural hardware has remained elusive because wave systems generally lack cascadable nonlinear neurons with signal regeneration and phase-robust operation. Here we demonstrate integrated magnonic neural circuits based on nonlinear threshold neurons realized in nanoscale yttrium iron garnet waveguides. The neurons perform weighted summation of multiple spin-wave inputs, while a pump-controlled nonlinear activation defines continuously tunable firing thresholds. Owing to deeply nonlinear spin-wave dynamics, the activated neurons emit self-normalized outputs whose intensities are largely independent of the input amplitudes, while nonlinear phase self-adjustment suppresses sensitivity to the relative input phases, enabling deterministic neuron-to-neuron cascading without external signal restoration. We experimentally realize programmable threshold neurons, reconfigurable weighted classification and deterministic cascading between sequential neuronal stages, and further demonstrate reconfigurable physical pattern recognition in a seven-neuron integrated magnonic circuit through experimental classification of the binary letter patterns 'HUST'. These results establish nonlinear magnons as a scalable platform for integrated neural hardware and position nonlinear wave dynamics as a general paradigm for physical neuromorphic computing. △ Less

Submitted 10 June, 2026; originally announced June 2026.

Comments: 17 pages, 5 figures

Programming nanomechanical computation with light

Authors: Xiaofei Guo, Jonne Drost, Fons van der Laan, Jesse J. Slim, Marc Serra-Garcia, Ewold Verhagen

Abstract: Looking at physical systems as computers allows us to regard physical properties, such as thermal noise, symmetry or topology, as unconventional resources for computation. However, harnessing these resources requires programming computational functionality through strong, controllable nonlinearities in the system. Here, we show that cavity optomechanical interactions allow laser-controlled computa… ▽ More Looking at physical systems as computers allows us to regard physical properties, such as thermal noise, symmetry or topology, as unconventional resources for computation. However, harnessing these resources requires programming computational functionality through strong, controllable nonlinearities in the system. Here, we show that cavity optomechanical interactions allow laser-controlled computation with nanomechanical degrees of freedom. We demonstrate a set of basic digital logic gates with level restoration and controlled mechanical couplings as essential ingredients for arbitrary computing networks. Owing to the strong optomechanical nonlinearity and precise readout, the system operates close to thermal amplitudes, in the regime where thermodynamic stochasticity governs its behavior. This opens a new path for the realization of physical computing with controlled nonlinear resonators. △ Less

Submitted 25 May, 2026; originally announced May 2026.

Approaching physical limits of latent dimensionality in optical computing

Authors: Zhenyu Zhao, Zijun Qiu, Xuan Hu, Yao Zhou, Jinlong Xiang, Youlve Chen, Chaojun Xu, Yuchen Yin, Tao Lin, Yikai Su, Xuhan Guo

Abstract: The physical implementation of artificial intelligence requires mapping computational processes onto the dynamic physical processes of the underlying computing platform. The photonic processors offer an intrinsically parallel and low energy framework for this mapping, however, a mismatch between the potential computing capability of a bounded optical domain and the human accessible manipulation ra… ▽ More The physical implementation of artificial intelligence requires mapping computational processes onto the dynamic physical processes of the underlying computing platform. The photonic processors offer an intrinsically parallel and low energy framework for this mapping, however, a mismatch between the potential computing capability of a bounded optical domain and the human accessible manipulation range sets a hard integration density ceiling on existing architectures. Here, we address this challenge by investigating the integration density limits in photonic processors through exploring the fundamental physical limits on the latent dimensionality for maximum expressivity of a bounded optical domain. These physical limits potentially serve as universal metrics for evaluating optical computing capacity. To validate these, we design and realize ultracompact multimode photonic processors approaching these limits: a 2.2 um by 8 um processor achieves 86.7 % accuracy in experiment for iris flower classification, and a 20.6 um by 44.8 um processor reaches 92.9% accuracy in handwritten digit recognition. Finally, we scale this architecture to highly complex tasks by implementing a generative diffusion model for image synthesis. By grounding photonic processor design in the wave physics origin of latent dimensionality, our results supply the missing theoretical reference point for optical computing architecture. △ Less

Submitted 22 May, 2026; originally announced May 2026.

Pulsed thermal annealing enables switching of chiral antiferromagnetic order with a sub-millitesla field in Mn$_3$Sn

Authors: Xiaokang Li, Jing Zhang, Xiaodong Guo, Zengwei Zhu

Abstract: The manipulation of antiferromagnetic (AFM) order is a central theme in modern spintronics. In this work, we achieve reliable switching of the chiral AFM state in the Weyl antiferromagnet Mn$_3$Sn using a heat pulse combined with a very small magnetic field as small as 0.1 mT. By systematically measuring the anomalous Hall effect (AHE) in high-quality single crystals, we show that the field needed… ▽ More The manipulation of antiferromagnetic (AFM) order is a central theme in modern spintronics. In this work, we achieve reliable switching of the chiral AFM state in the Weyl antiferromagnet Mn$_3$Sn using a heat pulse combined with a very small magnetic field as small as 0.1 mT. By systematically measuring the anomalous Hall effect (AHE) in high-quality single crystals, we show that the field needed for switching decreases as the temperature approaches the Néel temperature $T_N$, and vanishes at $T_N$. Pulsed thermal annealing above $T_N$ followed by cooling in a tiny external field enables full and reproducible switching of the magnetic octupole order. Our results show that thermal softening (heating above $T_N$ to temporarily remove the magnetic anisotropy) is a key step that lowers the energy barrier to nearly zero. This allows an extremely weak directional field (like the effective field from spin-orbit torque in thin-film devices) to set the final magnetic state during cooling. We also provide a simple model to estimate the temperature rise in nanoscale devices under current pulses, giving practical guidance for device design. This work highlights that thermal effects are not a side issue but an important partner to spin torques, and suggests that future work should take both into account. △ Less

Submitted 21 May, 2026; originally announced May 2026.

Comments: 7 pages, 3 figures

FiLark: a streaming-first software framework for end-to-end exploration, annotation, and algorithm integration in distributed acoustic sensing

Authors: Jintao Li, Weichang Li, Kai Tong, Xaingyu Guo

Abstract: Distributed acoustic sensing (DAS) systems generate continuous, ultra-high-channel-count data streams at rates that exceed the capabilities of conventional batch-oriented analysis frameworks. As a result, essential tasks such as interactive exploration of long-duration recordings, scalable event annotation, and real-time algorithm-in-the-loop monitoring remain inadequately supported by workflows b… ▽ More Distributed acoustic sensing (DAS) systems generate continuous, ultra-high-channel-count data streams at rates that exceed the capabilities of conventional batch-oriented analysis frameworks. As a result, essential tasks such as interactive exploration of long-duration recordings, scalable event annotation, and real-time algorithm-in-the-loop monitoring remain inadequately supported by workflows built around manually selected data segments and offline processing. This paper presents FiLark (Fiber Lark), a Python framework that applies a \emph{streaming-first} principle uniformly across data access, signal processing, visualization and monitoring for DAS. Instead of operating on manually selected data segments, FiLark presents any DAS sources-including continuous multi-file recordings-as a unified stream and builds all system components around that abstraction. An OpenGL-based ring-buffer renderer enables interactive browsing and visualization of arbitrarily long recordings with constant memory usage. An integrated annotation interface supports event labeling directly within continuous data streams, facilitating the creation of reproducible machine-learning-ready labeled datasets without offline preprocessing. The signal processing library includes temporal, spatial, spectral, and decomposition-based operators, with both CPU implementations and GPU-accelerated variants via PyTorch, alongside stateful chunked execution that preserves processing continuity and application semantics across segment boundaries. A standardized monitor interface further integrates streaming detectors and learning-based models into the visualization workflow. By sharing a common streaming abstraction across all layers, FiLark allows processing configurations and workflows developed interactively to transfer directly to scalable production pipelines without modification. △ Less

Submitted 19 May, 2026; originally announced May 2026.

Robust High-Precision Time Transfer over 91-km Hollow-Core Fiber: Immunity to Dispersion and Nonlinearity

Authors: Bo Liu, Xinxing Guo, Jiang Chen, Huibo Hong, Qian Zhou, Xiang Zhang, Ru Yuan, Rongduo Lu, Tao Liu, Ruifang Dong, Shougang Zhang

Abstract: To address the fundamental limitations imposed by chromatic dispersion and environmental susceptibility in standard single-mode fiber (SMF) for long-haul high-precision time transfer, we systematically explore the application potential of hollow-core fiber (HCF) through comparative experiments. We designed a bidirectional time transfer platform enabling direct comparison between HCF and SMF links… ▽ More To address the fundamental limitations imposed by chromatic dispersion and environmental susceptibility in standard single-mode fiber (SMF) for long-haul high-precision time transfer, we systematically explore the application potential of hollow-core fiber (HCF) through comparative experiments. We designed a bidirectional time transfer platform enabling direct comparison between HCF and SMF links across distances of 91 km, 68 km, and 54 km. We quantitatively characterize the impact of critical non-reciprocal error sources, specifically the optical Kerr effect and chromatic dispersion, under varying laser power, wavelength drift, and environmental perturbations. Our results show that HCF exhibits significantly suppressed dispersion, with a mean coefficient of 3.4 ps per nm per km, and reduced environmental sensitivity compared with SMF. Notably, over the 91 km link, the HCF yields a signal-to-noise ratio (SNR) enhancement of more than 24 dB and confines the time deviation to less than 80 ps, which is nearly an order-of-magnitude improvement over SMF, where the time deviation exceeds 600 ps, while remaining nearly immune to power and wavelength fluctuations. Under 24 hour diurnal monitoring, the 68 km HCF link demonstrates strong robustness, with environment-induced time delay fluctuations of 776 ps, corresponding to only 24.5% of those in SMF, which reach 3166 ps. Consequently, the time transfer stability, evaluated by time deviation (TDEV), reaches 0.2 ps at an integration time of 1000 s, representing a twofold improvement over SMF. These findings validate HCF as a superior transmission medium with low latency, low nonlinearity, and high thermal stability, paving the way for next-generation ultra-stable, long-haul time-frequency distribution networks. △ Less

Submitted 13 May, 2026; originally announced May 2026.

Comments: 10 pages, 8 figures

Projection of purification performance for the RELICS experiment

Authors: Jiachen Yu, Kaihang Li, Jingfan Gu, Chang Cai, Guocai Chen, Jiangyu Chen, Huayu Dai, Rundong Fang, Hongrui Gao, Fei Gao, Xiaoran Guo, Jiheng Guo, Chengjie Jia, Gaojun Jin, Fali Ju, Yanzhou Hao, Xu Han, Yang Lei, Meng Li, Minhua Li, Shengchao Li, Siyin Li, Tao Li, Qing Lin, Jiajun Liu , et al. (25 additional authors not shown)

Abstract: The RELICS (REactor neutrino LIquid xenon Coherent elastic Scattering) experiment employs a dual-phase liquid xenon time projection chamber to search for Coherent Elastic Neutrino-Nucleus Scattering (CE$ν$NS) induced by reactor neutrinos. To detect these sub-keV nuclear recoils and minimize signal attenuation, it is critical to maintain a sufficiently low impurity concentration in the detector. Th… ▽ More The RELICS (REactor neutrino LIquid xenon Coherent elastic Scattering) experiment employs a dual-phase liquid xenon time projection chamber to search for Coherent Elastic Neutrino-Nucleus Scattering (CE$ν$NS) induced by reactor neutrinos. To detect these sub-keV nuclear recoils and minimize signal attenuation, it is critical to maintain a sufficiently low impurity concentration in the detector. This work presents a comprehensive purity evolution model developed to describe impurity migration inside the detector. Utilizing measured material outgassing rates as input parameters, the model incorporates non-uniform transport mechanisms of the impurities, including circulation, vaporization, and condensation. The model is validated using data from a dedicated prototype detector. Based on this validated model, projections for the purification performance of the upcoming RELICS-10 and RELICS-50 detectors are provided. △ Less

Submitted 14 April, 2026; originally announced April 2026.

Enhanced electron injection for efficient proton acceleration and neutron production in femtosecond laser-driven nano-structured targets

Authors: Yingzi Dai, Chengyu Qin, Hui Zhang, Guoqiang Zhang, Changbo Fu, Xiangai Deng, Dirui Xu, Shuai Xu, Xuesong Geng, Jing Wang, Bowen Zhang, Yunwei Cui, Xiaojing Guo, Weifu Yin, Yanqi Liu, Xingyan Liu, Cheng Wang, Zongxin Zhang, Bingnan Shi, Lianghong Yu, Xiaoyan Liang, Yuxin Leng, Baifei Shen, Liangliang Ji, Ruxin Li

Abstract: Micro- or nano-structured targets are advantageous in enhancing and manipulating laser-proton acceleration, due to the increased absorption of laser energy and onset of direct laser acceleration for high-energy electrons. Here, we experimentally demonstrate that nano-wire-array printed on a flat substrate is an efficient nano-injector of relativistic electrons that leads to a significant boost of… ▽ More Micro- or nano-structured targets are advantageous in enhancing and manipulating laser-proton acceleration, due to the increased absorption of laser energy and onset of direct laser acceleration for high-energy electrons. Here, we experimentally demonstrate that nano-wire-array printed on a flat substrate is an efficient nano-injector of relativistic electrons that leads to a significant boost of laser-driven proton acceleration and neutron production beyond normal geometry. By employing an ultra-intense (2*1021 W/cm2) femtosecond laser pulse to irradiate nano-wire-array targets, protons with cut-off energies of 62.8 MeV are generated, and notably, the energy conversion efficiency from laser to protons reaches up to 9% - 3.5 times higher than that of flat foils. After bombarding a beryllium converter, 1.1*1010 neutrons are produced. Full 3D particle-in-cell simulations have reproduced experimental results and reveal interference mechanisms between the nano-wires and substrate, leading to continuous pumping of electrons from the substrate and standing-wave enhanced re-injection from the wire tip. This efficient injection finally results in the large sheath field and thus high yield of energetic protons and neutrons. Dependence on the wire length and scaling with laser amplitude are further discussed. These results suggest that 3D-printed structures are promising in developing compact laser-driven high-flux proton and neutron sources for numerous applications. △ Less

Submitted 3 April, 2026; originally announced April 2026.

Comments: 23 pages,6 figures

Project and Generate: Divergence-Free Neural Operators for Incompressible Flows

Authors: Xigui Li, Hongwei Zhang, Ruoxi Jiang, Deshu Chen, Chensen Lin, Limei Han, Yuan Qi, Xin Guo, Yuan Cheng

Abstract: Learning-based models for fluid dynamics often operate in unconstrained function spaces, leading to physically inadmissible, unstable simulations. While penalty-based methods offer soft regularization, they provide no structural guarantees, resulting in spurious divergence and long-term collapse. In this work, we introduce a unified framework that enforces the incompressible continuity equation as… ▽ More Learning-based models for fluid dynamics often operate in unconstrained function spaces, leading to physically inadmissible, unstable simulations. While penalty-based methods offer soft regularization, they provide no structural guarantees, resulting in spurious divergence and long-term collapse. In this work, we introduce a unified framework that enforces the incompressible continuity equation as a hard, intrinsic constraint for both deterministic and generative modeling. First, to project deterministic models onto the divergence-free subspace, we integrate a differentiable spectral Leray projection grounded in the Helmholtz-Hodge decomposition, which restricts the regression hypothesis space to physically admissible velocity fields. Second, to generate physically consistent distributions, we show that simply projecting model outputs is insufficient when the prior is incompatible. To address this, we construct a divergence-free Gaussian reference measure via a curl-based pushforward, ensuring the entire probability flow remains subspace-consistent by construction. Experiments on 2D Navier-Stokes equations demonstrate exact incompressibility up to discretization error and substantially improved stability and physical consistency. △ Less

Submitted 25 March, 2026; originally announced March 2026.

In-orbit Test of the Weak Equivalence Principle with Atom Interferometry

Authors: Dan-Fang Zhang, Jing-Ting Li, Wen-Zhang Wang, Wei-Hao Xu, Jia-Yi Wei, Xiao Li, Yi-Bo Wang, Dong-Feng Gao, Jia-Qi Zhong, Biao Tang, Lin Zhou, Run-Bing Li, Huan-Yao Sun, Qun-Feng Chen, Lei Qin, Mei-zhen An, Zong-Feng Li, Shu-Quan Wang, Xiao-Xiao Guo, Yao Tian, Xi-He Yu, Hong-En Zhong, Xi Chen, Jin Wang, Ming-Sheng Zhan

Abstract: The Weak Equivalence Principle (WEP) is a central pillar of general relativity. Its precise test with quantum systems in space offers a unique window onto new physics. Here we report the first in-orbit quantum test of the WEP. A dual-species (85Rb/87Rb) atom interferometer is realized aboard the China Space Station. Methods of platform motion suppression, fluorescence detection switching, and two-… ▽ More The Weak Equivalence Principle (WEP) is a central pillar of general relativity. Its precise test with quantum systems in space offers a unique window onto new physics. Here we report the first in-orbit quantum test of the WEP. A dual-species (85Rb/87Rb) atom interferometer is realized aboard the China Space Station. Methods of platform motion suppression, fluorescence detection switching, and two-photon detuning switching are developed to eliminate phase noise and improve measurement accuracy. A test uncertainty of 2.8*10-8 is obtained from 280 days of WEP test data, and a test result of (-3.1+/-4.6)*10-7 is achieved after error estimation. This improves prior atom-interferometric WEP tests in microgravity by three orders of magnitude. This work paves the way for space-borne quantum inertial sensors and their application to future fundamental physics in space. △ Less

Submitted 24 March, 2026; originally announced March 2026.

Comments: 22 pages,6 figures

Heterogeneously Integrated Diamond-on-Lithium Niobate Quantum Photonic Platform

Authors: Sophie W. Ding, Chang Jin, Zixi Li, Nicholas Achuthan, Kazuhiro Kuruma, Xinghan Guo, Brandon Grinkemeyer, David D. Awschalom, Nazar Delegan, F. Joseph Heremans, Alexander A. High, Marko Loncar

Abstract: Diamond photonics has enabled efficient interfaces for quantum memories and is predicted to be a critical component of quantum networks. However, scalable network architectures require spatial, temporal, and spectral control of photons, which relies on nonlinear and electro-optic functionalities that diamond alone cannot provide. Here, we demonstrate heterogeneous integration of a thin-film lithiu… ▽ More Diamond photonics has enabled efficient interfaces for quantum memories and is predicted to be a critical component of quantum networks. However, scalable network architectures require spatial, temporal, and spectral control of photons, which relies on nonlinear and electro-optic functionalities that diamond alone cannot provide. Here, we demonstrate heterogeneous integration of a thin-film lithium niobate (TFLN) platform, which has strong chi-2 nonlinearity and electro-optic effects, with thin diamond films. We demonstrate high-Q diamond photonic crystal cavities (Q factors exceeding 5x10^4 at 735 nm) that are lithographically aligned with TFLN photonic backbone and critically coupled to it. This allows us to realize low-loss diamond-TFLN "escalators" (loss ~1 dB/coupler) that support efficient light transfer between them. At cryogenic temperatures (5K), we can collect photons emitted from silicon vacancies (SiVs) embedded within the diamond structure via the TFLN photonic circuit. This approach establishes a scalable route toward integrated photonic circuits for practical quantum networking and other technologies. △ Less

Submitted 9 March, 2026; originally announced March 2026.

Thermal stable nonlinear Raman-Nath diffraction and Cherenkov radiation in PPKTP crystals

Authors: Tao Xie, YangMing Liu, WenXin Zhu, XueShi Guo, RuiBo Jin

Abstract: Nonlinear Raman-Nath diffraction (NRND) and nonlinear Cherenkov radiation (NCR) are significant nonlinear diffraction phenomena in optics. Previous studies have primarily focused on NRND and NCR in uniaxial crystals, particularly in periodically poled lithium niobate (PPLN) crystals. However, research on these phenomena in biaxial crystals, such as periodically poled potassium titanyl phosphate (P… ▽ More Nonlinear Raman-Nath diffraction (NRND) and nonlinear Cherenkov radiation (NCR) are significant nonlinear diffraction phenomena in optics. Previous studies have primarily focused on NRND and NCR in uniaxial crystals, particularly in periodically poled lithium niobate (PPLN) crystals. However, research on these phenomena in biaxial crystals, such as periodically poled potassium titanyl phosphate (PPKTP), has been limited, and the study of NCR in PPKTP has not yet been undertaken.In this work, we experimentally investigated NRND and NCR phenomena in PPKTP crystals under varying incident angles, pump polarizations, poling periods, and crystal temperatures. Our findings indicate that PPKTP exhibits over ten times greater thermal stability compared to PPLN. This high thermal stability is promising for applications in parallel optical computing, as it helps reduce optical mode deviations and minimize bit error rates. △ Less

Submitted 5 March, 2026; originally announced March 2026.

Hardware Implementation of Photonic Spiking Hash Retrieval

Authors: Shangxuan Shi, Shuiying Xiang, Xintao Zeng, Yonghang Chen, Wanting Yu, Yahui Zhang, Xingxing Guo, Yue Hao

Abstract: Hashing retrieval is a pivotal technology for large-scale similarity search, widely applied in retrieval-augmented generation (RAG) for large language models (LLMs), massive image repositories, and bioinformatics sequence matching. However, traditional electronic hashing implementations face severe bottlenecks in power consumption and latency when processing high-dimensional data, while existing p… ▽ More Hashing retrieval is a pivotal technology for large-scale similarity search, widely applied in retrieval-augmented generation (RAG) for large language models (LLMs), massive image repositories, and bioinformatics sequence matching. However, traditional electronic hashing implementations face severe bottlenecks in power consumption and latency when processing high-dimensional data, while existing photonic neural networks often lack robust mechanisms for direct binary code generation under analog noise. To address these challenges, we propose a hardware-software co-designed photonic spiking hashing framework. We utilize the nonlinear thresholding dynamics of a distributed feedback laser with saturable absorber (DFB-SA) to realize the final binarization of a single-step spiking neural network (SNN). Crucially, a hardware-aware quantization margin loss is introduced to maximize the decision margin, effectively mitigating bit flips caused by optical intensity fluctuations. Validated on MNIST (image) and 20 Newsgroups (text) datasets, our system demonstrates robust binary code generation and high retrieval accuracy comparable to digital baselines. Most significantly, the proposed photonic architecture exhibits superior efficiency with an encoding latency of 2.294 ns/query and an energy consumption of 73.70 pJ/query. This work offers a robust and viable path for ultra-fast, energy-efficient optoelectronic neuromorphic computing in high-throughput information retrieval tasks. △ Less

Submitted 3 March, 2026; originally announced March 2026.

Comments: 9 pages

Quantum cascade laser roadmap

Authors: Carlo Silvestri, Aleksandar D. Rakić, Dragan Indjin, Ali Khalatpour, Christian Jirauschek, Aleksandar Demic, Zoran Ikonic, Paul Dean, Nikola Vuković, Jelena Radovanović, Lianhe Li, Edmund Linfield, Michael Jaidl, Karl Unterrainer, Giacomo Scalari, Jérôme Faist, Lorenzo Luigi Columbo, Massimo Brambilla, Marco Piccardo, Sukhdeep Dhillon, Mithun Roy, David Burghoff, Karl Bertling, Jari Torniainen, Xiaoqiong Qi , et al. (24 additional authors not shown)

Abstract: Quantum cascade lasers (QCLs) are unipolar semiconductor lasers first demonstrated in 1994. Since then, they have played a central role in advancing mid-infrared and terahertz photonics, becoming among the most reliable light sources in these regions of the electromagnetic spectrum. Their importance is further reinforced by their ability to generate self-starting optical frequency combs, whose inv… ▽ More Quantum cascade lasers (QCLs) are unipolar semiconductor lasers first demonstrated in 1994. Since then, they have played a central role in advancing mid-infrared and terahertz photonics, becoming among the most reliable light sources in these regions of the electromagnetic spectrum. Their importance is further reinforced by their ability to generate self-starting optical frequency combs, whose investigation is motivated both by fundamental physics and by a wide range of applications, including molecular spectroscopy and free-space optical communications. This Roadmap provides a unified overview of current advances and emerging directions in QCL research. The chapters are organized into three main sections: device design and technology; frequency combs and pulse formation; and applications of QCLs. Each chapter reviews the relevant background, summarizes the current state of the art, and identifies key challenges and future directions within its specific research area. △ Less

Submitted 18 February, 2026; originally announced February 2026.

Photonic spiking reinforcement learning for intelligent routing

Authors: Shuiying Xiang, Yonghang Chen, Ling Zheng, Zhicong Tu, Xintao Zeng, Mengting Yu, Shuai Wang, Yahui Zhang, Xingxing Guo, Weitao Pan, Yue Hao

Abstract: Intelligent routing plays a key role in modern communication infrastructure, including data centers, computing networks, and future 6G networks. Although reinforcement learning (RL) has shown great potential for intelligent routing, its practical deployment remains constrained by high energy consumption and decision latency. Here, we propose a photonic spiking RL architecture that implements a pro… ▽ More Intelligent routing plays a key role in modern communication infrastructure, including data centers, computing networks, and future 6G networks. Although reinforcement learning (RL) has shown great potential for intelligent routing, its practical deployment remains constrained by high energy consumption and decision latency. Here, we propose a photonic spiking RL architecture that implements a proximal policy optimization (PPO)-based intelligent routing algorithm. The performance of the proposed approach is systematically evaluated on a software-defined network (SDN) with a fat-tree topology. The results demonstrate that, under various baseline traffic rate conditions, the PPO-based routing strategy significantly outperforms the conventional Dijkstra algorithm in several key performance metrics. Furthermore, a hardware-software collaborative framework of the spiking Actor network is realized for three typical baseline traffic rates, utilizing a photonic synapse chip based on a Mach-Zehnder interferometer (MZI) array and a photonic spiking neuron chip based on distributed feedback lasers with a saturable absorber (DFB-SAs). Experimental validation on 640 state-action pairs shows that the inference accuracy of the hardware-software collaborative framework is consistent with that of the pure algorithmic implementation. The impacts of different hidden-layer scales in the spiking Actor network and varying network size of fat-tree topology are further analyzed. The integration of photonic spiking RL with SDN-based routing establishes a novel paradigm for intelligent routing optimization, featuring ultra-low latency and high energy efficiency. This approach exhibits broad application prospects in real-time network optimization scenarios, including large-scale data centers, computing networks, satellite Internet systems, and future 6G networks. △ Less

Submitted 1 February, 2026; originally announced February 2026.

Comments: 22 pages, 7 figures, submitted to Opto-Electronic Science (under review)

Hardware implementation of photonic neuromorphic autonomous navigation

Authors: Yonghang Chen, Shuiying Xiang, Xintao Zeng, Mengting Yu, Tao Zou, Shangxuan Shi, Xingxing Guo, Yanan Han, Yahui Zhang, Yue Hao

Abstract: Reinforcement learning (RL) is a core technology enabling the transition of artificial intelligence (AI) from perception to decision-making, but its deployment on conventional electronic hardware suffers from high latency and energy consumption imposed by the von Neumann architecture. Here, we propose a photonic spiking twin delayed deep deterministic policy gradient (TD3) reinforcement learning a… ▽ More Reinforcement learning (RL) is a core technology enabling the transition of artificial intelligence (AI) from perception to decision-making, but its deployment on conventional electronic hardware suffers from high latency and energy consumption imposed by the von Neumann architecture. Here, we propose a photonic spiking twin delayed deep deterministic policy gradient (TD3) reinforcement learning architecture for neuromorphic autonomous navigation and experimentally validate it on a distributed feedback laser with a saturable absorber (DFB-SA) array. The hybrid architecture integrates a photonic spiking Actor network with dual continuous-valued Critic networks, where the final nonlinear spiking activation layer of the Actor is deployed on the DFB-SA laser array. In autonomous navigation tasks, the system achieves an average reward of 58.22 plus-minus 17.29 and a success rate of 80% plus-minus 8.3%. Hardware-software co-inference demonstrates an estimated energy consumption of 0.78 nJ/inf and an ultra-low latency of 191.20 ps/inf, with co-inference error rates of 0.051% and 0.059% in task scenarios with and without obstacle interference, respectively. Simulations for error-activated channels show full agreement with the expected responses, validating the dynamic characteristics of the DFB-SA laser. The architecture shows strong potential for integration with large-scale photonic linear computing chips, enabling fully-functional photonic computation and low-power, low-latency neuromorphic autonomous navigation. △ Less

Submitted 1 February, 2026; originally announced February 2026.

Comments: 29 pages, 5 figures; submitted to Laser & Photonics Reviews (under review)

LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics

Authors: Qisong Xiao, Xinhai Chen, Qinglin Wang, Xiaowei Guo, Binglin Wang, Weifeng Chen, Zhichao Wang, Yunfei Liu, Rui Xia, Hang Zou, Gencheng Liu, Shuai Li, Jie Liu

Abstract: Deep learning has emerged as a promising paradigm for spatio-temporal modeling of fluid dynamics. However, existing approaches often suffer from limited generalization to unseen flow conditions and typically require retraining when applied to new scenarios. In this paper, we present LLM4Fluid, a spatio-temporal prediction framework that leverages Large Language Models (LLMs) as generalizable neura… ▽ More Deep learning has emerged as a promising paradigm for spatio-temporal modeling of fluid dynamics. However, existing approaches often suffer from limited generalization to unseen flow conditions and typically require retraining when applied to new scenarios. In this paper, we present LLM4Fluid, a spatio-temporal prediction framework that leverages Large Language Models (LLMs) as generalizable neural solvers for fluid dynamics. The framework first compresses high-dimensional flow fields into a compact latent space via reduced-order modeling enhanced with a physics-informed disentanglement mechanism, effectively mitigating spatial feature entanglement while preserving essential flow structures. A pretrained LLM then serves as a temporal processor, autoregressively predicting the dynamics of physical sequences with time series prompts. To bridge the modality gap between prompts and physical sequences, which can otherwise degrade prediction accuracy, we propose a dedicated modality alignment strategy that resolves representational mismatch and stabilizes long-term prediction. Extensive experiments across diverse flow scenarios demonstrate that LLM4Fluid functions as a robust and generalizable neural solver without retraining, achieving state-of-the-art accuracy while exhibiting powerful zero-shot and in-context learning capabilities. Code and datasets are publicly available at https://github.com/qisongxiao/LLM4Fluid. △ Less

Submitted 29 January, 2026; originally announced January 2026.

CM-GAI: Continuum Mechanistic Generative Artificial Intelligence Theory for Data Dynamics

Authors: Shan Tang, Ziwei Cao, Zhenling Yang, Jiachen Guo, Yicheng Lu, Wing Kam Liu, Xu Guo

Abstract: Generative artificial intelligence (GAI) plays a fundamental role in high-impact AI-based systems such as SORA and AlphaFold. Currently, GAI shows limited capability in the specialized domains due to data scarcity. In this paper, we develop a continuum mechanics-based theoretical framework to generalize the optimal transport theory from pure mathematics, which can be used to describe the dynamics… ▽ More Generative artificial intelligence (GAI) plays a fundamental role in high-impact AI-based systems such as SORA and AlphaFold. Currently, GAI shows limited capability in the specialized domains due to data scarcity. In this paper, we develop a continuum mechanics-based theoretical framework to generalize the optimal transport theory from pure mathematics, which can be used to describe the dynamics of data, realizing the generative tasks with a small amount of data. The developed theory is used to solve three typical problem involved in many mechanical designs and engineering applications: at material level, how to generate the stress-strain response outside the range of experimental conditions based on experimentally measured stress-strain data; at structure level, how to generate the temperature-dependent stress fields under the thermal loading; at system level, how to generate the plastic strain fields under transient dynamic loading. Our results show the proposed theory can complete the generation successfully, showing its potential to solve many difficult problems involved in engineering applications, not limited to mechanics problems, such as image generation. The present work shows that mechanics can provide new tools for computer science. The limitation of the proposed theory is also discussed. △ Less

Submitted 28 January, 2026; originally announced January 2026.

On-chip Multimode Opto-electronic Neural Network

Authors: Jinlong Xiang, Youlve Chen, Chaojun Xu, Yuchen Yin, Yufeng Zhang, Yikai Su, Zhipei Sun, Xuhan Guo

Abstract: Opto-electronic computing combines the complementary strengths of photonics and electronics to deliver ultrahigh computational throughput with high energy efficiency. However, its practical deployment for real-world applications has been limited by architectures that rely on delicate wavelength management or phase-sensitive coherent detection. Here, we demonstrate the first multimode opto-electron… ▽ More Opto-electronic computing combines the complementary strengths of photonics and electronics to deliver ultrahigh computational throughput with high energy efficiency. However, its practical deployment for real-world applications has been limited by architectures that rely on delicate wavelength management or phase-sensitive coherent detection. Here, we demonstrate the first multimode opto-electronic neural network (MOENN) on a silicon-on-insulator platform. By utilizing orthogonal waveguide eigenmodes as independent information carriers, our architecture achieves robust single-wavelength computation that is inherently immune to spectral crosstalk and phase noise. The fabricated MOENN chip monolithically integrates all functional components, including input encoders, programmable mode-division fan-in/-out units, and most importantly, the nonlinear multimode activation functions. We report the system's versatility through in-situ training via a genetic algorithm, successfully resolving the nonlinear decision boundaries of a two-class dataset and achieving 92.1% accuracy on the Iris classification benchmark. Furthermore, we reconfigure the MOENN into a one-dimensional convolutional neural network, attaining an accuracy of 90.7% on the electrocardiogram-based emotion recognition task. This work establishes a new opto-electronic computing paradigm of simple control and excellent robustness, providing a compelling path toward scalable, deployable photonic intelligence. △ Less

Submitted 22 January, 2026; originally announced January 2026.

Bio-Inspired Photonic Spectral Encoders

Authors: Yujia Zhang, Xiangfu Lei, Yinpeng Chen, Chaojun Xu, Hanxiao Cui, Tawfique Hasan, Yikai Su, Zongyin Yang, Zhipei Sun, Xuhan Guo

Abstract: Compact spectrometers promise to revolutionize sensing applications, offering a unique pathway to laboratory-grade analysis within a miniaturized footprint. Central to their performance is the encoding strategy to unknown spectra, which determines the efficiency, accuracy, and adaptability of spectral reconstruction. However, the absence of a unified spectral encoding framework has hindered the re… ▽ More Compact spectrometers promise to revolutionize sensing applications, offering a unique pathway to laboratory-grade analysis within a miniaturized footprint. Central to their performance is the encoding strategy to unknown spectra, which determines the efficiency, accuracy, and adaptability of spectral reconstruction. However, the absence of a unified spectral encoding framework has hindered the realization of optimal, high-performance compact spectrometers. We propose a transformative approach: an information-theoretic framework grounded in bio-inspired Bayesian expected information gain that defines the first generic light encoder for computational spectrometers. By optimizing three fundamental attributes at the lowest level of physical hierarchy, (1) orthogonality, (2) completeness, and (3) sparsity, we establish a design paradigm that transcends conventional encoding hardware limitations. We validate this paradigm with the first generic encoder capable of dynamically reconfiguring its response matrices. Experiments show superior reconstruction fidelity across diverse spectral regimes, enabling tunable spectral encoding tailored to varied input features. An ultra-high resolution of 6 pm and a broad measurable bandwidth of 30 nm are experimentally validated. By bridging the gap between theoretical encoding principles and reconfigurable hardware, our framework defines a coherent basis for future advances in compact spectrometry. △ Less

Submitted 17 January, 2026; originally announced January 2026.

Comparative study of equilibrium and non-equilibrium predictions by different models for a hypersonic cone at high-altitude

Authors: Mengyu Wang, Pan Yan, Qin Li, Zhenfeng Wang, Xiaoming Guo, Yuanchun Liu

Abstract: Targeting a cone with the half-angle as 10-deg at M = 27 and H = 72 km, simulations were conducted comparatively to analyze the predictions by different equilibrium and non-equilibrium gas models. Following validation and grid studies, systematic comparisons on aerodynamic performance, flow structures, and characteristic distributions were performed. The key findings are: (1) While the overall flo… ▽ More Targeting a cone with the half-angle as 10-deg at M = 27 and H = 72 km, simulations were conducted comparatively to analyze the predictions by different equilibrium and non-equilibrium gas models. Following validation and grid studies, systematic comparisons on aerodynamic performance, flow structures, and characteristic distributions were performed. The key findings are: (1) While the overall flow structures are broadly similar, discrepancies exist in the features at the base locations, e.g., the diverse high-temperature distributions. Notably, the vibrational temperatures distribute differently under slip and non-slip boundary conditions near the wall; (2) The equilibrium gas model predicts higher drag coefficient, wall heat flux, and skin friction than those of non-equilibrium models. Predictions also vary among the non-equilibrium models themselves. Specifically, compared to the three-temperature model, the one- and two-temperature models predict larger drag coefficients with the relative difference exceeding 5%. Nevertheless, the results from the three-temperature model with and without slip conditions are largely consistent; (3) The disparities between equilibrium and non-equilibrium characteristics are primarily manifested in the shock layer and wake regions. Within these regions, the overall temperature for the equilibrium gas is lower than that for the non-equilibrium cases, while in the latter specific non-equilibrium features are distinctly exhibited, e.g., the translational-rotational temperature is generally higher than that from the one-temperature model, and the vibrational-electronic temperature shows the opposite trend. Notably, in the slip flow within the three-temperature model, the translational-rotational temperature is higher and, particularly, the vibrational temperature is even larger than counterparts of the non-slip flows near the wall and base center line. △ Less

Submitted 14 January, 2026; originally announced January 2026.

DC response of an interferometer topology with an L-shaped cavity: a tabletop study

Authors: Junlang Li, Jiehong Huang, Xinyao Guo, Haixing Miao, Yuchao Chen, Xiaoman Huang, Yuan Pan, Chenjie Zhou, Raffaele Flaminio, Jameson Graef Rollins, Bram Slagmolen, Fan Zhang, Teng Zhang, Mengyao Wang

Abstract: A new interferometer topology for kilohertz gravitational-wave detection was recently proposed in [Zhang et al. Phys. Rev. X 13, 021019 (2023)]. The design is based on an L-shaped optical cavity pumped through a Sagnac-like vortex. We report a tabletop experiment that characterizes the interferometer's optical response near DC. When the laser frequency is locked to the resonance of the L-shaped ca… ▽ More A new interferometer topology for kilohertz gravitational-wave detection was recently proposed in [Zhang et al. Phys. Rev. X 13, 021019 (2023)]. The design is based on an L-shaped optical cavity pumped through a Sagnac-like vortex. We report a tabletop experiment that characterizes the interferometer's optical response near DC. When the laser frequency is locked to the resonance of the L-shaped cavity, we observe that the cavity input coupler becomes effectively transparent, yielding a simple Michelson-like response. Moreover, the Sagnac vortex separates into upper and lower paths, which behave as two independent pumping paths driving the cavity. These observations are in agreement with theoretical predictions. Our results provide an intuitive physical picture of this interferometer topology and offer insight into its lock acquisition strategy. △ Less

Submitted 6 June, 2026; v1 submitted 14 January, 2026; originally announced January 2026.

Comments: 7pages, 4figures. The internal review process is completed, we re-uploaded the paper now

Search for Cosmic Ray Electron Boosted Dark Matter with the CDEX-10 Experiment

Authors: R. Xu, L. T. Yang, Q. Yue, K. J. Kang, Y. J. Li, H. P. An, Greeshma C., J. P. Chang, H. Chen, Y. H. Chen, J. P. Cheng, J. Y. Cui, W. H. Dai, Z. Deng, Y. X. Dong, C. H. Fang, H. Gong, Q. J. Guo, T. Guo, X. Y. Guo, L. He, J. R. He, H. X. Huang, T. C. Huang, S. Karmakar , et al. (63 additional authors not shown)

Abstract: We present new constraints on the cosmic ray electron boosted light dark matter (CReDM) using the 205.4 kg$\cdot$day data of the CDEX-10 experiment located at the China Jinping Underground Laboratory. The cosmic ray electron spectrum and distribution in the Galaxy are generated by the $\tt GALPROP$ code package. In the calculation process of DM-electron scattering process in the Galaxy, we conside… ▽ More We present new constraints on the cosmic ray electron boosted light dark matter (CReDM) using the 205.4 kg$\cdot$day data of the CDEX-10 experiment located at the China Jinping Underground Laboratory. The cosmic ray electron spectrum and distribution in the Galaxy are generated by the $\tt GALPROP$ code package. In the calculation process of DM-electron scattering process in the Galaxy, we consider the energy-dependency of the DM-electron scattering cross section. The constraints on CReDM are set for both heavy and light mediator scenarios using the CDEX-10 dataset. The result exceeds previous Standard Halo Model (SHM) limits for DM mass lower than 0.6 MeV in heavy mediator case and corresponds to the best sensitivity among all direct detection experiments from 1 keV to 0.5 MeV in the light mediator scenario. △ Less

Submitted 13 January, 2026; originally announced January 2026.

Comments: 7 pages, 5 figures

Full-bandwidth, continuous, and grayscale 3D nanolithography via line-illumination temporal focusing of ultrafast lasers

Authors: Qiuyuan Zhong, Charudatta Datar, Wei Liu, Gan Liu, Xiangsen Guo, Xuhao Fan, Fei Han, Bingxu Chen, Songyun Gu, Shih-Chi Chen

Abstract: Achieving fast and continuous fabrication of large-scale complex 3D structures is key to unlocking industrial-scale adoption of two-photon lithography (TPL). Despite substantial improvement in peak optical patterning rates enabled by recent parallel exposure strategies, the practical fabrication rate of TPL for large structures remains low. This gap is primarily attributed to the mismatched bandwi… ▽ More Achieving fast and continuous fabrication of large-scale complex 3D structures is key to unlocking industrial-scale adoption of two-photon lithography (TPL). Despite substantial improvement in peak optical patterning rates enabled by recent parallel exposure strategies, the practical fabrication rate of TPL for large structures remains low. This gap is primarily attributed to the mismatched bandwidth among toolpath generation, data transferring, and laser patterning, and the stop-and-go operation for part stitching etc. Here, we present a line-illumination temporal focusing TPL (Line-TF TPL) solution that, for the first time, demonstrates true continuous 3D nanolithography with full-bandwidth data streaming, grayscale voxel tuning, and cost-effective large-scale fabrication capability. To achieve the goal, we use a digital micromirror device (DMD) to temporally focus femtosecond laser pulses into a programmable line with enhanced 3D resolution, pixel-level grayscale control, and a high-refresh rate (>10 kHz), realizing continuous fabrication at a hardware-limited maximum rate. Specifically, we fabricated centimeter-scale 3D structures with sub-diffraction features down to 75 nm laterally and 99 nm axially. Our method eliminates stitching defects by continuous scanning and grayscale stitching; and provides real-time pattern streaming at a bandwidth that is one order of magnitude higher than previous TPL systems. The line-scanning strategy also substantially lowers the pulse-energy requirement, hence the cost for parallel TPL; and maximizes the machine uptime through continuous operation, both of which are critical metrics for industrialization. Finally, we demonstrated centimeter-scale artworks, fine 3D features, and complex miniaturized optics, revealing the Line-TF TPL's large-scale application potential in photonic packaging, metamaterial discovery, and biomedicine. △ Less

Submitted 27 December, 2025; originally announced December 2025.

Comments: 23 pages, 5 figures

Photonic Spiking Graph Neural Network for Energy-Efficient Structured Data Processing

Authors: Wanting Yu, Shuiying Xiang, Xingxing Guo, Shangxuan Shi, Haowen Zhao, Xintao Zeng, Yahui Zhang, Hongbo Jiang, Yue Hao

Abstract: Photonic computing shows great potential for signal processing and artificial intelligence (AI) acceleration due to its ultra-high speed, low energy consumption, and inherent parallelism. Existing photonic computing research has mainly focused on convolutional neural networks (CNNs) and fully connected neural networks (FCNNs), which are well suited for tasks such as image classification and object… ▽ More Photonic computing shows great potential for signal processing and artificial intelligence (AI) acceleration due to its ultra-high speed, low energy consumption, and inherent parallelism. Existing photonic computing research has mainly focused on convolutional neural networks (CNNs) and fully connected neural networks (FCNNs), which are well suited for tasks such as image classification and object detection but face limitations in handling graph-structured data. Graph neural networks (GNNs) are specifically designed to model complex relational structures. In this work, we propose a photonic spiking graph neural network (PSGNN) architecture that integrates the structural modeling capability of GNNs, the temporal dynamics of spiking neurons, and the parallel computing advantages of photonic hardware. Through hardware-software co-optimization, a bias-term simulation method tailored for photonic chips is implemented using feature-dimension expansion, enabling effective network training. Experiments on the KarateClub and PubMed datasets achieve training accuracies of 100 percent (92 +/- 2 percent) and test accuracies of 97 percent (90 +/- 1 percent). A silicon photonics 4 x 4 Mach-Zehnder interferometer (MZI) array is further constructed for hardware validation, achieving a test accuracy of 93 percent. The system demonstrates an inference latency of 97 ps, with an energy efficiency of 280 GOPS/W and a computational density of 52 GOPS/mm^2. These results highlight the potential of PSGNN for structured-data processing applications. △ Less

Submitted 22 December, 2025; originally announced December 2025.

Comments: 10Pages 10figures

Decoding Molecular Geometries in Coulomb Explosion Imaging via Physics-Informed Deep Neural Network

Authors: Xingyu Guo, Enliang Wang, Wenguang Wu, Zhaopeng Xing, Tuo Liu, Chunkai Xu, Xu Shan, Artem Rudenko, Daniel Rolles, Jing Chen, Xiangjun Chen

Abstract: Determining the absolute configuration of gas-phase molecules in position-space has long been a fundamental challenge in molecular physics. While strong-field-induced Coulomb explosion imaging (CEI) has emerged as a powerful tool for probing molecular stereochemistry in momentum-space, reconstructing the original three-dimensional structure of polyatomic molecules remains a long-standing challenge… ▽ More Determining the absolute configuration of gas-phase molecules in position-space has long been a fundamental challenge in molecular physics. While strong-field-induced Coulomb explosion imaging (CEI) has emerged as a powerful tool for probing molecular stereochemistry in momentum-space, reconstructing the original three-dimensional structure of polyatomic molecules remains a long-standing challenge due to the inherent complexity of multidimensional inversion. Here, we introduce a deep learning framework that bridges this gap by directly recovering position-space molecular structures from Coulomb explosion momentum patterns. Our approach combines CEI simulations with a neural network trained to establish the mapping between momentum-space Newton plots and real-space geometries. The trained model demonstrates high fidelity in reconstructing the structure of CHF$_3$ from experimental CEI data. This generalizable framework can not only be extended to other molecular systems but also opens avenues for time-resolved structural analysis of molecular dynamics. △ Less

Submitted 18 December, 2025; originally announced December 2025.

Deep Photonic Reservoir Computing with On-chip Nonlinearity

Authors: Jinlong Xiang, Youlve Chen, Yuchen Yin, Zhenyu Zhao, Chaojun Xu, An He, Xintong Lv, Yikai Su, Xuhan Guo

Abstract: Reservoir computing, renowned for its low training cost, has emerged as a promising lightweight paradigm for efficient spatiotemporal processing,it remains challenging to realize deep photonic reservoir computing (DPRC) systems, due to the lack of scalable on-chip nonlinearity. Here, we introduce a versatile time delayed DPRC framework that natively supports deep and concurrent spatiotemporal proc… ▽ More Reservoir computing, renowned for its low training cost, has emerged as a promising lightweight paradigm for efficient spatiotemporal processing,it remains challenging to realize deep photonic reservoir computing (DPRC) systems, due to the lack of scalable on-chip nonlinearity. Here, we introduce a versatile time delayed DPRC framework that natively supports deep and concurrent spatiotemporal processing entirely in the optical domain. At its core, the system leverages free carrier dynamics in silicon microring resonators to provide the fundamental nonlinearity and short term memory, and these nonlinear nodes are interconnected through true time delay lines that establish shared long-term memory. Benefiting from intrinsic physical nonlinearity and multi-timescale fading memory, this simple yet effective architecture demonstrates remarkable high dimensional representation capabilities. On the NTU RGB D benchmark, the parameter efficient DPRC system achieves superior action recognition accuracies compared to mainstream deep learning models, while requiring only a single shot regression training procedure. We further verify a prototype DPRC chip that excels across diverse dataset classification and time series prediction tasks. It enables a streamlined all optical pipeline between hierarchical layers, delivering a consistent computational density of 334.25 TOPs/mm2, independent of the reservoir depth and three orders of magnitude higher than conventional approaches. Moreover, its performance scales with near-zero hardware overhead by utilizing additional wavelength channels. This DPRC network is highly scalable on a silicon photonic platform, with flexible extension to hundreds of deep reservoir layers and parallel channels, paving the way toward intelligent optoelectronic systems for advanced real time processing and parallel decision making. △ Less

Submitted 11 December, 2025; originally announced December 2025.

Comments: 5 Figures

Quantum Simulations of Opinion Dynamics

Authors: Xingyu Guo, Xiaoyang Wang, Lingxiao Wang

Abstract: Consensus formation is a central problem in collective behavior. In this work, we develop quantum models of opinion dynamics that can be exactly solved and implemented on current quantum hardware. By exploiting quantum superposition, measurement-induced state collapse, and entanglement, our framework captures key features of opinion evolution and allows a systematic investigation of how network co… ▽ More Consensus formation is a central problem in collective behavior. In this work, we develop quantum models of opinion dynamics that can be exactly solved and implemented on current quantum hardware. By exploiting quantum superposition, measurement-induced state collapse, and entanglement, our framework captures key features of opinion evolution and allows a systematic investigation of how network connectivity shapes consensus formation. We demonstrate our approach using practical quantum circuits and validate representative cases on IBM Quantum devices for the open-chain. These findings pave the way for further exploration into quantum-enhanced social modeling, highlighting the potential of near-term quantum computers for simulating collective behavior in complex systems. △ Less

Submitted 7 March, 2026; v1 submitted 3 December, 2025; originally announced December 2025.

Comments: 9 pages, 11 figures. Update results for different connectivities and references

Report number: RIKEN-iTHEMS-Report-25

Hardware-Software Collaborative Computing of Photonic Spiking Reinforcement Learning for Robotic Continuous Control

Authors: Mengting Yu, Shuiying Xiang, Changjian Xie, Yonghang Chen, Haowen Zhao, Xingxing Guo, Yahui Zhang, Yanan Han, Yue Hao

Abstract: Robotic continuous control tasks impose stringent demands on the energy efficiency and latency of computing architectures due to their high-dimensional state spaces and real-time interaction requirements. Conventional electronic computing platforms face computational bottlenecks, whereas the fusion of photonic computing and spiking reinforcement learning (RL) offers a promising alternative. Here,… ▽ More Robotic continuous control tasks impose stringent demands on the energy efficiency and latency of computing architectures due to their high-dimensional state spaces and real-time interaction requirements. Conventional electronic computing platforms face computational bottlenecks, whereas the fusion of photonic computing and spiking reinforcement learning (RL) offers a promising alternative. Here, we propose a novel computing architecture based on photonic spiking RL, which integrates the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm with spiking neural network (SNN). The proposed architecture employs an optical-electronic hybrid computing paradigm wherein a silicon photonic Mach-Zehnder interferometer (MZI) chip executes linear matrix computations, while nonlinear spiking activations are performed in the electronic domain. Experimental validation on the Pendulum-v1 and HalfCheetah-v2 benchmarks demonstrates the system capability for software-hardware co-inference, achieving a control policy reward of 5831 on HalfCheetah-v2, a 23.33% reduction in convergence steps, and an action deviation below 2.2%. Notably, this work represents the first application of a programmable MZI photonic computing chip to robotic continuous control tasks, attaining an energy efficiency of 1.39 TOPS/W and an ultralow computational latency of 120 ps. Such performance underscores the promise of photonic spiking RL for real-time decision-making in autonomous and industrial robotic systems. △ Less

Submitted 29 November, 2025; originally announced December 2025.

Hardware-aware Lightweight Photonic Spiking Neural Network for Pattern Classification

Authors: Shuiying Xiang, Yahui Zhang, Shangxuan Shi, Haowen Zhao, Dianzhuang Zheng, Xingxing Guo, Yanan Han, Ye Tian, Liyue Zhang, Yuechun Shi, Yue Hao

Abstract: There exists a significant scale gap between photonic neural network integrated chips and neural networks, which hinders the deployment and application of photonic neural network. Here, we propose hardware-aware lightweight spiking neural networks (SNNs) architecture tailored to our photonic neuromorphic chips, and conducts hardware-software collaborative computing for solving patter classificatio… ▽ More There exists a significant scale gap between photonic neural network integrated chips and neural networks, which hinders the deployment and application of photonic neural network. Here, we propose hardware-aware lightweight spiking neural networks (SNNs) architecture tailored to our photonic neuromorphic chips, and conducts hardware-software collaborative computing for solving patter classification tasks. Here, we employed a simplified Mach-Zehnder interferometer (MZI) mesh for performing linear computation, and 16-channel distributed feedback lasers with saturable absorber (DFB-SA) array for performing nonlinear spike activation. Both photonic neuromorphic chips based on the MZI mesh and DFB-SA array were designed, optimized and fabricated. Furthermore, we propose a lightweight spiking neural network (SNN) with discrete cosine transform to reduce input dimension and match the input/output ports number of the photonic neuromorphic chips. We demonstrated an end-to-end inference of an entire layer of the lightweight photonic SNN. The hardware-software collaborative inference accuracy is 90% and 80.5% for MNIST and Fashion-MNIST datasets, respectively. The energy efficiency is 1.39 TOPS/W for the MZI mesh, and is 987.65 GOPS/W for the DFB-SA array. The lightweight architecture and experimental demonstration address the challenge of scale mismatch between the photonic chip and SNN, paving the way for the hardware deployment of photonic SNNs. △ Less

Submitted 29 November, 2025; originally announced December 2025.

Universal convolution from wave dynamics: photonic processing and encryption in synthetic dimension

Authors: Xiaolong Su, Weiwei Liu, Ruiqian Cheng, Haoru Zhang, Xinyao Guo, He Huang, Chengzhi Qin, Peixiang Lu, Bing Wang

Abstract: Convolution, a cornerstone of signal processing and optical neural networks, has traditionally been implemented by mapping mathematical operations onto complex hardware. Here, we overcome this challenge by revealing that wave dynamics in translation-symmetric lattices intrinsically performs convolution, with the dispersion relation uniquely defining the complex-valued kernel. Leveraging this unive… ▽ More Convolution, a cornerstone of signal processing and optical neural networks, has traditionally been implemented by mapping mathematical operations onto complex hardware. Here, we overcome this challenge by revealing that wave dynamics in translation-symmetric lattices intrinsically performs convolution, with the dispersion relation uniquely defining the complex-valued kernel. Leveraging this universal principle, we develop a convolutional architecture of minimal complexity through wave evolution in programmable photonic synthetic lattices, delivering high-throughput, multifunctional capabilities at a rate of 13.5 tera-operations per second (TOPS) for image processing. Beyond convolution acceleration, the kernel's complex nature facilitates the photonic simulation of both irreversible diffusion and reversible unitary quantum dynamics under classical incoherent excitation. Capitalizing on the physics-based reversibility and undetectable phase information, we demonstrate a novel convolution-driven optical encryption strategy. This work establishes a unified framework for photonic computing by grounding convolution in wave dynamics, opening avenues toward scalable, multifunctional photonic processors with high integration potential. △ Less

Submitted 27 November, 2025; originally announced November 2025.

Development of a dual-phase xenon time projection chamber prototype for the RELICS experiment

Authors: Lingfeng Xie, Jiajun Liu, Yifei Zhao, Chang Cai, Guocai Chen, Jiangyu Chen, Huayu Dai, Rundong Fang, Hongrui Gao, Fei Gao, Jingfan Gu, Xiaoran Guo, Jiheng Guo, Chengjie Jia, Gaojun Jin, Fali Ju, Yanzhou Hao, Xu Han, Yang Lei, Kaihang Li, Meng Li, Minhua Li, Ruize Li, Shengchao Li, Siyin Li , et al. (28 additional authors not shown)

Abstract: The RELICS (REactor neutrino LIquid xenon Coherent elastic Scattering) experiment aims to detect coherent elastic neutrino-nucleus scattering from reactor antineutrinos using a dual-phase xenon time projection chamber. To validate the detector concept and ensure technical reliability for the full-scale experiment, a dedicated prototype was designed, constructed, and operated. This work presents an… ▽ More The RELICS (REactor neutrino LIquid xenon Coherent elastic Scattering) experiment aims to detect coherent elastic neutrino-nucleus scattering from reactor antineutrinos using a dual-phase xenon time projection chamber. To validate the detector concept and ensure technical reliability for the full-scale experiment, a dedicated prototype was designed, constructed, and operated. This work presents an overview of the design, construction, and operational performance of the prototype, with emphasis on its major subsystems, including the TPC, cryogenic and xenon purification systems, slow control, and data acquisition. During operation, the detector demonstrated the capability to achieve a sub-keV energy threshold required for the RELICS physics program, as reflected by a measured single electron gain of 34.30~$\pm$~0.01~(stat.)~PE/e$^-$ and the successful detection of 0.27~keV L-shell decay events from $^{37}$Ar. In addition, essential data analysis techniques and simulation frameworks were developed and validated, establishing the methodological foundation for future RELICS operations. The successful construction and operation of this prototype confirm the feasibility of the core technologies and provide a crucial experimental basis for the final RELICS detector. △ Less

Submitted 11 March, 2026; v1 submitted 23 November, 2025; originally announced November 2025.

Re-examining the boundary conditions in modelling SAW-driven acoustofluidic streaming

Authors: Qinran Wei, Suyu Ding, Yang Zhao, Yuanpeng Ma, Dachuan Sang, Dong Zhang, Xiasheng Guo

Abstract: Numerical simulations of surface acoustic wave (SAW)-induced acoustic streaming are highly sensitive to the choice of second-order boundary conditions. This study systematically compares the no-slip (NS) and Stokes slip (SD) boundary conditions through different numerical approaches. Two- and three-dimensional simulations based on the Reynolds stress method are performed for standing SAW and trave… ▽ More Numerical simulations of surface acoustic wave (SAW)-induced acoustic streaming are highly sensitive to the choice of second-order boundary conditions. This study systematically compares the no-slip (NS) and Stokes slip (SD) boundary conditions through different numerical approaches. Two- and three-dimensional simulations based on the Reynolds stress method are performed for standing SAW and travelling SAW devices. Results are validated against particle image velocimetry measurements of streaming patterns and velocities. We show that the SD condition yields Lagrangian velocity fields in significantly better agreement with experiments than the NS condition, accurately capturing vortex number, rotation direction, and amplitude across varying device geometries and operating conditions. In contrast, the NS condition overpredicts velocities by 1-2 orders of magnitude and often fails to reproduce experimentally observed vortex structures. These findings highlight the essential role of the Stokes drift boundary condition in modelling acoustic streaming and provide clear guidance for its use in future simulations of SAW-based acoustofluidic systems. △ Less

Submitted 16 November, 2025; originally announced November 2025.

Comments: 16 pages, 13 figures

Scattering Induced Mode Chirality in Ring Resonators

Authors: Haochen Yan, Xu Guo, Arghadeep Pal, Xiaoyuan Huang, Alekhya Ghosh, Lewis Hill, Shuangyou Zhang, Nivedita Vishnukumar, Toby Bi, Masoud Kheyri, Jianming Mai, Hao Zhang, Yaojing Zhang, Jolly Xavier, Haihua Fan, Kok Wai Cheah, Peter Littlewood, Pascal DelHaye

Abstract: Non-Hermitian physics can be used to break time reversal symmetry and is important for interactions in a wide range of systems, from active matter and neural networks to metamaterials and non-equilibrium thermodynamics. In integrated photonic devices, non-Hermitian physics can be used for direction-dependent light propagation, reconfigurable light paths, selective energy localization and optical i… ▽ More Non-Hermitian physics can be used to break time reversal symmetry and is important for interactions in a wide range of systems, from active matter and neural networks to metamaterials and non-equilibrium thermodynamics. In integrated photonic devices, non-Hermitian physics can be used for direction-dependent light propagation, reconfigurable light paths, selective energy localization and optical isolators. In this work, we report previously unexplored direction-dependent mode splitting in ring microresonators, achieved by adding multiple scatterers around the cavity. Through experiments, simulations, and theoretical modeling, we unveil the underlying physics that changes the resonance shapes in resonant systems with backscattering. By engineering the spatial configuration of the scatterers, we can produce a predictable and repeatable direction-dependent mode splitting, enabling new ways to route light through optical resonators and photonic networks. In addition, the direction dependent mode-splitting can be used for precise near-field measurements, enhancing traditional sensing in integrated photonic chips. △ Less

Submitted 10 November, 2025; originally announced November 2025.

Multiplexed Catheter-Integrated Pressure Sensing System for Endoluminal Interventions

Authors: Xiaotong Guo, Qindong Zheng, Jinshi Zhao, Bing Li, Eric Morgan Yeatman

Abstract: Advances in flexible catheters pave the way for minimally invasive diagnosis and treatment of luminal organs and tubular structures through endoluminal interventions. A key challenge is in establishing non-constraining pressure monitoring at the interfaces between medical catheters and intraluminal anatomy exhibiting curvilinear contours, structural variability, and time-dependent physiological mo… ▽ More Advances in flexible catheters pave the way for minimally invasive diagnosis and treatment of luminal organs and tubular structures through endoluminal interventions. A key challenge is in establishing non-constraining pressure monitoring at the interfaces between medical catheters and intraluminal anatomy exhibiting curvilinear contours, structural variability, and time-dependent physiological motion. This work presents a scalable and multi-purpose pressure sensing system for multidirectional monitoring of tissue interactions, establishing a robust solution for deploying diagnostic and therapeutic instruments in various types of endoluminal interventions. This approach provides an integrated system encompassing pressure sensors, catheters, and signal acquisition devices. A poly (vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) film is miniaturized and configured into a multiplexed piezoelectric-based pressure sensor, providing flexibility and scalability in conforming to medical catheters with curved surfaces. The catheter is fabricated with a cost-effective and highly scalable fiber drawing technology, establishing a means of fast prototyping catheters with bespoke structures for sensor integration and medical instrument integration. The system achieves enhanced pressure detection sensitivity and a comparable sensing range, compared with state-of-the-art catheter-integrated sensors. Through in-vitro phantom studies, the system performs precise multi-directional sensing within various clinical endoluminal scenarios, showing its potential in digitalizing tissue interactions during endoluminal interventions. △ Less

Submitted 2 November, 2025; originally announced November 2025.

Design and characterization of a photosensor system for the RELICS experiment

Authors: Jijun Yang, Ruize Li, Chang Cai, Guocai Chen, Jiangyu Chen, Huayu Dai, Rundong Fang, Fei Gao, Jingfan Gu, Xiaoran Guo, Jiheng Guo, Gaojun Jin, Fali Ju, Yanzhou Hao, Yang Lei, Kaihang Li, Meng Li, Minhua Li, Shengchao Li, Siyin Li, Tao Li, Qing Lin, Jiajun Liu, Sheng Lv, Guang Luo , et al. (23 additional authors not shown)

Abstract: In this paper, we present the design and characterization of a photosensor system developed for the RELICS experiment. An extended dynamic range base was designed to mitigate photomultiplier tube (PMT) saturation caused by intense cosmic muon backgrounds in the surface-level RELICS detector. The system employs dual readout from the anode and the seventh dynode to extend the linear response range o… ▽ More In this paper, we present the design and characterization of a photosensor system developed for the RELICS experiment. An extended dynamic range base was designed to mitigate photomultiplier tube (PMT) saturation caused by intense cosmic muon backgrounds in the surface-level RELICS detector. The system employs dual readout from the anode and the seventh dynode to extend the linear response range of the PMT. In particular, our characterization and measurements of Hamamatsu R8520-406 PMTs confirm stable operation under positive high-voltage bias, extending the linear response range by more than an order of magnitude. Furthermore, a model of PMT saturation and recovery was developed to evaluate the influence of cosmic muon signals in the RELICS detector. The results demonstrate the system capability to detect coherent elastic neutrino-nucleus scattering signals under surface-level cosmic backgrounds, and suggest the potential to extend the scientific reach of RELICS to MeV-scale interactions. △ Less

Submitted 19 February, 2026; v1 submitted 28 October, 2025; originally announced October 2025.

Comments: 19 pages, 13 figures. v3: updated to address the referees' comments, fixed author list typo. Journal reference added

Journal ref: JINST 21 (2026) 2, P02039

Constraints on ultraheavy dark matter from the CDEX-10 experiment at the China Jinping Underground Laboratory

Authors: Y. F. Wang, L. T. Yang, Q. Yue, K. J. Kang, Y. J. Li, H. P. An, Greeshma C., J. P. Chang, H. Chen, Y. H. Chen, J. P. Cheng, J. Y. Cui, W. H. Dai, Z. Deng, Y. X. Dong, C. H. Fang, H. Gong, Q. J. Guo, T. Guo, X. Y. Guo, L. He, J. R. He, H. X. Huang, T. C. Huang, S. Karmakar , et al. (63 additional authors not shown)

Abstract: We report a search for ultraheavy dark matter (UHDM) with the CDEX-10 experiment at the China Jinping Underground Laboratory. Using a Monte Carlo framework that incorporates Earth shielding effects, we simulated UHDM propagation and energy deposition in p-type point-contact germanium detectors. Analysis of 205.4 kg$\cdot$day exposure in the 0.16--4.16 keVee range showed no excess above background.… ▽ More We report a search for ultraheavy dark matter (UHDM) with the CDEX-10 experiment at the China Jinping Underground Laboratory. Using a Monte Carlo framework that incorporates Earth shielding effects, we simulated UHDM propagation and energy deposition in p-type point-contact germanium detectors. Analysis of 205.4 kg$\cdot$day exposure in the 0.16--4.16 keVee range showed no excess above background. Our results exclude the spin-independent UHDM-nucleon scattering with two cross section scales, with the UHDM mass from $10^6$ to $10^{11}$ GeV, and provide the most stringent constraints with solid-state detectors below $10^8$ GeV. △ Less

Submitted 28 March, 2026; v1 submitted 24 October, 2025; originally announced October 2025.

Comments: 8 pages, 5 figures. Version updated to match PRD version

Journal ref: Phys. Rev. D 113, 052011 (2026)

Universal loss and gain characterization inside photonic integrated circuits

Authors: Haoran Chen, Ruxuan Liu, Gedalia Y. Koehler, Fatemehsadat Tabatabaei, Xiangwen Guo, Shuman Sun, Zijiao Yang, Beichen Wang, Andreas Beling, Xu Yi

Abstract: Integrated photonics has undergone tremendous development in the past few decades, transforming many fields of study in science and technology. Loss and gain are two fundamental elements in photonic circuits and have direct impacts on nearly all key performance metrics. Surprisingly, the tools to characterize the optical loss and gain inside photonic integrated circuits (PICs) are very limited. Th… ▽ More Integrated photonics has undergone tremendous development in the past few decades, transforming many fields of study in science and technology. Loss and gain are two fundamental elements in photonic circuits and have direct impacts on nearly all key performance metrics. Surprisingly, the tools to characterize the optical loss and gain inside photonic integrated circuits (PICs) are very limited. This is because, unlike free-space or fiber optics, integrated circuits cannot be nondestructively disassembled. Here, we report a universal method to see inside the photonic integrated circuits and measure loss and gain on the component level nondestructively. The method leverages nonlinear optical devices as optical power discriminators to retrieve the loss and gain information inside the PICs. Our method has a precision better than 0.1 dB, and can characterize the loss of individual fiber-chip coupling facet and general unknown devices under test. As a demonstration of applications, we measured the true on-chip quantum efficiency of a quantum PIC consisting of heterogeneously integrated balanced photodiodes, a critical building block for integrated quantum technology. Our method can be implemented on different photonic platforms, and can be used to understand gain and loss in complex photonic circuits, which is essential to optimize circuit design and to create large-scale systems with predictable, reproducible performance. △ Less

Submitted 20 October, 2025; originally announced October 2025.