arXiv subjects

Xuhan Guo

Publications and source records attributed to Xuhan Guo.

At least 19 recordsLinked to original sources

arXiv2026

Artificial Intelligence in a Photonic Temporal Processor

Optical neural networks (ONNs) promise high-throughput and energy-efficient artificial intelligence, yet essentially all implementations so far encode information across space either in free-space arrays or in integrated waveguide meshes, tying the number of neurons to the number of physical components and fixes the routing topology at fabrication. Here we show that moving the computation into time decouples computational dimension from hardware dimension. Exploiting space-time duality, we implement optical diffraction and interference entirely in time domain, using thin-film lithium niobate modulators as time lenses and temporal masks, with chromatic dispersion providing the coupling between successive temporal neurons. We experimentally verify high-order, complex-valued matrix-matrix multiplications using just a single optical input/output port, scaling the computational dimensions far beyond the channel count. By incorporating optical feedback, we extend this platform into versatile neural networks, where the network layers, neuron numbers, and synaptic connections are fully programmable and in-situ trainable. Our temporal diffractive neural networks are successfully validated on various classification benchmarks, alongside image and video generation tasks. Notably, using this platform we demonstrate an all-analogue generative pipeline in which the latent variable is drawn directly from amplified spontaneous emission, so that no digital sampling or electronic modulation appears anywhere in the generative path. Furthermore, high-resolution images and videos are generated at high frame rates, outperforming state-of-the-art modulator-refresh-limited optical generative systems. These results establish a unified photonic temporal computing framework, providing a scalable and deployable pathway toward next-generation machine intelligence.

physics.optics↗

arXiv2026

Approaching physical limits of latent dimensionality in optical computing

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.

physics.optics↗

arXiv2026

On-chip Multimode Opto-electronic Neural Network

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.

physics.optics↗

arXiv2026

Bio-Inspired Photonic Spectral Encoders

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.

physics.optics↗

arXiv2025

Deep Photonic Reservoir Computing with On-chip Nonlinearity

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.

physics.optics↗

arXiv2025

Reconfigurable non-Abelian geometric phase in hybrid integrated photonics

The non-Abelian geometric phase possesses the capability of enabling robust and fault-resilient unitary transformations, making it a cornerstone of holonomic quantum computation. This "all-geometric" approach has successfully advanced the manipulation of electrons in condensed matter physics and has sparked growing interest in its implementation within photonics, an area that has traditionally relied on sensitive dynamic phases. However, a major limitation of the topologically protected and inherently robust geometric phase is its lack of reconfigurability. In contrast, mainstream optical computing schemes demand high reconfigurability to compensate for fabrication errors and to support diverse computational tasks. Here, we demonstrate a reconfigurable non-Abelian geometric phase based on the non-volatile phase-change material Sb$_2$Se$_3$. By switching between its crystalline and amorphous states, the number of degenerate subspaces can be actively adjusted. Thus, multilevel second-order matrices and reconfigurable third-order matrices with 3-bit control is realized. For larger reconfigurable rotation angles, tunable braiding operations are also demonstrated. Furthermore, high-dimensional reconfigurable braiding shows promising potential for applications in optical switching. Our results pave the way for the all-geometric-phase-based approach in optical computing.

physics.optics↗

arXiv2025

Observation of generic U(m) non-Abelian holonomy in photonics

Non-Abelian geometric phases form the foundation of fault-tolerant holonomic quantum computation. An "all-geometric" approach leveraging these phases enables robust unitary operations in condensed matter systems. Photonics, with rich degrees of freedom, offer a highly promising platform for non-Abelian holonomy. Yet, achieving universal unitary transformations in photonic holonomy remain elusive. Intrinsic positive real couplings in dissipationless photonic waveguides restrict holonomy to special orthogonal matrices, falling short of universal quantum gates or arbitrary linear operations. Here, we introduce artificial gauge fields (AGFs) to enable complex-valued couplings, expanding photonic holonomy to the full unitary group. We realize generic U(2) transformations and synthesize higher dimensional U(m) operations (up to U(4)) in integrated photonics. Our results open doors toward the transformative "all-geometric-phase" approach in photonic computing in both classical and quantum realms.

physics.optics↗

arXiv2025

Miniaturized Chaos-assisted Spectrometer

Computational spectrometers are at the forefront of spectroscopy, promising portable, on-chip, or in-situ spectrum analysis through the integration of advanced computational techniques into optical systems. However, existing computational spectrometer systems have yet to fully exploit optical properties due to imperfect spectral responses, resulting in increased system complexity and compromised performance in resolution, bandwidth, and footprint. In this study, we introduce optical chaos into spectrum manipulation via cavity deformation, leveraging high spatial and spectral complexities to address this challenge. By utilizing a single chaotic cavity, we achieve high diversity in spectra, facilitating channel decorrelation of 10 pm and ensuring optimal reconstruction over 100 nm within an ultra-compact footprint of 20*22 um2 as well as an ultra-low power consumption of 16.5 mW. Our approach not only enables state-of-the-art on-chip spectrometer performance in resolution-bandwidth-footprint metric, but also has the potential to revolutionize the entire computational spectrometer ecosystem.

physics.optics↗

arXiv2025

High computational density nanophotonic media for machine learning inference

Efficient machine learning inference is essential for the rapid adoption of artificial intelligence across various domains.On-chip optical computing has emerged as a transformative solution for accelerating machine learning tasks, owing to its ultra-low power consumption. However, enhancing the computational density of on-chip optical systems remains a significant challenge, primarily due to the difficulties in miniaturizing and integrating key optical interference components.In this work, we harness the potential of fabrication-constrained scattering optical computing within nanophotonic media to address these limitations.Central to our approach is the use of fabrication-aware inverse design techniques, which enable the realization of manufacturable on-chip scattering structures under practical constraints.This results in an ultra-compact optical neural computing architecture with an area of just 64 um2,representing a remarkable three orders of magnitude reduction in footprint compared to traditional optical neural networks. Our prototype, tested on the Iris flower dataset, achieved an experimental accuracy of 86.7%, closely matching the simulation benchmark.This breakthrough showcases a promising pathway toward ultra-dense, energy-efficient optical processors for scalable machine learning inference, significantly reducing both the hardware footprint, latency, and power consumption of next-generation AI applications.

physics.optics↗

arXiv2025

GHz spiking neuromorphic photonic chip with in-situ training

Neuromorphic photonic computing represents a paradigm shift for next-generation machine intelligence, yet critical gaps persist in emulating the brain's event-driven, asynchronous dynamics,a fundamental barrier to unlocking its full potential. Here, we report a milestone advancement of a photonic spiking neural network (PSNN) chip, the first to achieve full-stack brain-inspired computing on a complementary metal oxide semiconductor-compatible silicon platform. The PSNN features transformative innovations of gigahertz-scale nonlinear spiking dynamics,in situ learning capacity with supervised synaptic plasticity, and informative event representations with retina-inspired spike encoding, resolving the long-standing challenges in spatiotemporal data integration and energy-efficient dynamic processing. By leveraging its frame-free, event-driven working manner,the neuromorphic optoelectronic system achieves 80% accuracy on the KTH video recognition dataset while operating at ~100x faster processing speeds than conventional frame-based approaches. This work represents a leap for neuromorphic computing in a scalable photonic platform with low latency and high throughput, paving the way for advanced applications in real-time dynamic vision processing and adaptive decision-making, such as autonomous vehicles and robotic navigation.

physics.optics↗

arXiv2025

Direct tensor processing with coherent light

Tensor processing is the cornerstone of modern technological advancements, powering critical applications in data analytics and artificial intelligence. While optical computing offers exceptional advantages in bandwidth, parallelism, and energy efficiency, existing methods optimized for scalar operations struggle to efficiently handle tensor-based tasks, limiting their applicability in complex applications, such as neural networks. Here, we report Parallel Optical Matrix Matrix Multiplication (POMMM), a novel paradigm that enables fully parallel tensor processing through a single coherent light propagation. This approach addresses key limitations of current optical methods, scaling the performance with data dimension, while improving theoretical computational power and efficiency. We demonstrate its high consistency with GPU based matrix matrix multiplication across both real-valued and complex valued domains. Moreover, we showcase its adaptability, scalability, and versatility in tensor processing applications such as convolutional and vision transformer neural networks. Furthermore, we analyse the theoretical compatibility and efficiency of POMMM in relation to existing optical computing paradigms, highlighting its potential to outperform current state-of-the-art methods. By enabling a variety of computational tasks and supporting multi2 wavelength and large-scale expansion, POMMM provides a scalable, high-efficient foundation for advancing next-generation optical computing.

physics.optics↗

arXiv2024

Ultra-compact beam steering nanolasers

The miniaturization and integration of beam steering devices have consistently been the focus of the field. Conventional methods alter the eigenmode of the optical cavity by regulating the refractive index. Due to the weak nonlinear effect of the optical system, the device must be sufficiently large to achieve sufficient light modulation. The effective method for miniaturizing beam steering devices currently in use is based on metasurfaces. However, this type of device necessitates the input of a laser source, which precludes the simultaneous generation and control of light in a single device. Here we propose a miniaturized beam steering device that employs mode selection between different bound states in the continuum (BIC) states through phase change material. The device is capable of simultaneously achieving both light generation and beam steering (33°) in a single device with a size of only 25 μm and with a low threshold of 8.9 kW cm-2. Furthermore,it is possible to achieve a significant degree of dynamic wavelength tunability, with a range extending up to 296 nm. This method achieves high-efficient regulation of light properties by dynamically controlling the system's topological charge, circumventing the problem of weak nonlinearity in traditional methods. Furthermore, the integration of phase change materials with nanolasers enables the direct alteration of lasing properties, which provides a novel idea for dynamic light control. The device process scheme based on phase change materials is straightforward, direct, and highly compatible, which will be advantageous for its intended application.

physics.optics↗

arXiv2024

Metasurface spectrometers beyond resolution-sensitivity constraints

Optical spectroscopy plays an essential role across scientific research and industry for non-contact materials analysis1-3, increasingly through in-situ or portable platforms4-6. However, when considering low-light-level applications, conventional spectrometer designs necessitate a compromise between their resolution and sensitivity7,8, especially as device and detector dimensions are scaled down. Here, we report on a miniaturizable spectrometer platform where light throughput onto the detector is instead enhanced as the resolution is increased. This planar, CMOS-compatible platform is based around metasurface encoders designed to exhibit photonic bound states in the continuum9, where operational range can be altered or extended simply through adjusting geometric parameters. This system can enhance photon collection efficiency by up to two orders of magnitude versus conventional designs; we demonstrate this sensitivity advantage through ultra-low-intensity fluorescent and astrophotonic spectroscopy. This work represents a step forward for the practical utility of spectrometers, affording a route to integrated, chip-based devices that maintain high resolution and SNR without requiring prohibitively long integration times.

physics.optics↗

arXiv2023

Miniaturized Computational Photonic Molecule Spectrometer

Miniaturized spectrometry system is playing an essential role for materials analysis in the development of in-situ or portable sensing platforms across research and industry. However, there unavoidably exists trade-offs between the resolution and operation bandwidth as the device scale down. Here, we report an extreme miniaturized computational photonic molecule (PM) spectrometer utilizing the diverse spectral characteristics and mode-hybridization effect of split eigenfrequencies and super-modes, which effectively eliminates the inherent periodicity and expands operation bandwidth with ultra-high spectral resolution. These results of dynamic control of the frequency, amplitude, and phase of photons in the photonic multi-atomic systems, pave the way to the development of benchtop sensing platforms for applications previously unfeasible due to resolution-bandwidth-footprint limitations, such as in gas sensing or nanoscale biomedical sensing.

physics.optics↗

arXiv2023

High-dimensional broadband non-Abelian holonomy in silicon nitride photonics

Non-Abelian geometry phase has attracted significant attention for the robust holonomic unitary behavior exhibited, which arises from the degenerate subspace evolving along a trajectory in Hilbert space. It has been regarded as a promising approach for implementing topologically protected quantum computation and logic manipulation. However, due to the challenges associated with high-dimensional parameters manipulation, this matrix-valued geometry phase has not been realized on silicon integrated photonic platform, which is CMOS compatible and regarded as the most promising flatform for next-generation functional devices. Here, we demonstrate the first non-Abelian holonomic high-dimensional unitary matrices on multilayer silicon nitride integrated platform. By leveraging the advantage of integrated platform and geometry phase, ultracompact footprint, highest order (up to six) and broadband operation (larger than 100nm) non-Abelian holonomy unitary matrices are experimentally realized. Our work paves the way for versatile non-Abelian optical computing devices in integrated photonics.

physics.optics↗

arXiv2022

Monolithic Integration of Embedded III-V Lasers on SOI

Silicon photonic integration has gained great success in many application fields owing to the excellent optical device properties and complementary metal-oxide semiconductor (CMOS) compatibility. Realizing monolithic integration of III-V lasers and silicon photonic components on single silicon wafer is recognized as a long-standing obstacle for ultra-dense photonic integration, which can provide considerable economical, energy efficient and foundry-scalable on-chip light sources, that has not been reported yet. Here, we demonstrate embedded InAs/GaAs quantum dot (QD) lasers directly grown on trenched silicon-on-insulator (SOI) substrate, enabling monolithic integration with butt-coupled silicon waveguides. By utilizing the patterned grating structures inside pre-defined SOI trenches and unique epitaxial method via molecular beam epitaxy (MBE), high-performance embedded InAs QD lasers with out-coupled silicon waveguide are achieved on such template. By resolving the epitaxy and fabrication challenges in such monolithic integrated architecture, embedded III-V lasers on SOI with continuous-wave lasing up to 85 oC are obtained. The maximum output power of 6.8 mW can be measured from the end tip of the butt-coupled silicon waveguides, with estimated coupling efficiency of approximately -7.35 dB. The results presented here provide a scalable and low-cost epitaxial method for realization of on-chip light sources directly coupling to the silicon photonic components for future high-density photonic integration.

physics.optics↗

arXiv2020

Universal programmable on-chip metasurface building blocks for arbitrary high-order mode manipulation

On-chip mode-division multiplexing (MDM) has been emerging as a promising technology to further enhance the link capacity and bandwidth of data communications with multiple mode channels. Both mode converters and mode exchangers are indispensable fundamental components for flexible mode operations. While several configurations have been developed previously, it is still very challenging to efficiently manipulate arbitrary high-order modes in a versatile way to reduce the R&D and prototyping costs. Here we initiate a breakthrough with a simple yet universal generic mode operator building block concept utilizing metasurface structures. The programmable arbitrary high-order mode operators can realize mode conversion and exchange simultaneously with competitive and uniform performance, high stability and compact footprints, offering a quintessential step change for on-chip multimode optical interconnections.

physics.optics↗

arXiv2019

All-Optical Spiking Neuron Based On Passive Micro-Resonator

Neuromorphic photonics that aims to process and store information simultaneously like human brains has emerged as a promising alternative for the next generation intelligent computing systems. The implementation of hardware emulating the basic functionality of neurons and synapses is the fundamental work in this field. However, previously proposed optical neurons implemented with SOA-MZIs, modulators, lasers or phase change materials are all dependent on active devices and quite difficult for integration. Meanwhile, although the nonlinearity in nanocavities has long been of interest, the previous theories are intended for specific situations, e.g., self-pulsation in microrings, and there is still a lack of systematic studies in the excitability behavior of the nanocavities including the silicon photonic crystal cavities. Here, we report for the first time a universal coupled mode theory model for all side-coupled passive microresonators. Attributed to the nonlinear excitability, the passive microresonator can function as a new type of all-optical spiking neuron. We demonstrate the microresonator-based neuron can exhibit the three most important characteristics of spiking neurons: excitability threshold, refractory period and cascadability behavior, paving the way to realize all-optical spiking neural networks.

physics.optics↗