arXiv subjects

Satoshi Sunada

Publications and source records attributed to Satoshi Sunada.

At least 19 recordsLinked to original sources

arXiv2026

Photonic reservoir computing with complex networks

Photonic reservoir computing has attracted increasing attention as a fast and low-cost approach for time-series prediction. Photonic reservoir computing utilizes the high speed, broad bandwidth, and spatial parallelism of light. However, the effect of the internal connection structure (network topology) on the computing performance has not been investigated for large-scale photonic reservoirs. In this study, we experimentally and numerically demonstrate photonic reservoir computing using a spatial light modulator to systematically evaluate the relationship between the network topology and the performance of reservoir computing. We introduce complex network structures such as small-world and scale-free network topologies of the internal nodes in the reservoir. We perform the memory capacity measurement and the one-step-ahead prediction task of the chaotic time series to compare the performance. We found that the small-world network exhibits the maximum memory capacity and the best prediction performance. Our numerical calculations reveal that the performance of the time-series prediction can be optimized by changing the rewiring probability of the network and the leak rate of the reservoir. We also implement photonic human brain network as a reservoir, which is designed by the connectomes of human brain activities. We found that the network topology strongly affects the performance of reservoir computing, and the small-world network structure outperforms the other configurations.

physics.optics↗

arXiv2026

Physical Reservoir Signal Acquisition for Sub-Nyquist Waveform Reconstruction

Physical reservoir computing has traditionally exploited the dynamics of physical systems for computation, enabling tasks such as inference, classification, and prediction. Here, we introduce a fundamentally different paradigm for exploiting physical reservoirs, termed "reservoir signal acquisition" (RSA), in which a physical reservoir serves as a dynamical measurement device rather than a computational engine. In RSA, the reservoir transforms an unknown broadband waveform into a diverse set of measurements, enabling waveform reconstruction from low-rate samples beyond the Nyquist limit of any individual acquisition channel. We show that exact reconstruction of arbitrary broadband signals is achieved when the number of measurement channels satisfies $M \geq N_R$, where $N_R$ is the undersampling ratio. Moreover, spectrally or temporally sparse signals can be recovered even when $M \ll N_R$, demonstrating a compressed-sensing capability that naturally emerges from the diversity of reservoir dynamics. We experimentally validate RSA using a silicon photonic reservoir circuit. With a data-driven calibration requiring no physical model of the device, we reconstruct radio-frequency signals up to 12.5 GHz using only low-rate analog-to-digital converters (ADCs), corresponding to four times the Nyquist frequency of each ADC. These results establish RSA as a new signal acquisition paradigm based on physical reservoirs, extending their role from computation to sub-Nyquist acquisition of broadband waveforms.

cs.ET↗

arXiv2026

General-Purpose Nonlinear Function Approximation via Linear Integrated Photonics

Photonic computing has emerged as a promising platform for accelerating artificial intelligence workloads by enabling low-latency and energy-efficient linear operations such as vector-matrix multiplication. However, scalable on-chip high-order nonlinear processing remains challenging, limiting the functional versatility of current photonic hardware. Here, we present an optoelectronic approach for approximating high-order and high-dimensional nonlinear functions. The key to this approach lies in optical random Fourier feature mapping, which transforms nonlinear function evaluation into an equivalent linear computation. This approach enables nonlinear computing within a linear photonic framework, eliminating the need for complex optical nonlinear or active materials while preserving scalability and computational throughput in a simple silicon photonic circuit. We experimentally demonstrate a broad class of nonlinear functions, including tenth-order Legendre polynomials, computationally demanding special functions (Voigt, Fermi-Dirac, and Fresnel), neural-network activation functions, two-dimensional nonlinear functions, and a 10-dimensional softmax layer. This work establishes a general and scalable strategy for nonlinear computing in photonic integrated hardware and opens a pathway toward fully functional optical accelerators for next-generation computing systems.

cs.ET↗

arXiv2026

A thin and soft optical tactile sensor for highly sensitive object perception

Tactile sensing is crucial in robotics and wearable devices for safe perception and interaction with the environment. Optical tactile sensors have emerged as promising solutions, as they are immune to electromagnetic interference and have high spatial resolution. However, existing optical approaches, particularly vision-based tactile sensors, rely on complex optical assemblies that involve lenses and cameras, resulting in bulky, rigid, and alignment-sensitive designs. In this study, we present a thin, compact, and soft optical tactile sensor featuring an alignment-free configuration. The soft optical sensor operates by capturing deformation-induced changes in speckle patterns generated within a soft silicone material, thereby enabling precise force measurements and texture recognition via machine learning. The experimental results show a root-mean-square error of 40 mN in the force measurement and a classification accuracy of 93.33% over nine classes of textured surfaces, including Mahjong tiles. The proposed speckle-based approach provides a compact, easily fabricated, and mechanically compliant platform that bridges optical sensing with flexible shape-adaptive architectures, thereby demonstrating its potential as a novel tactile-sensing paradigm for soft robotics and wearable haptic interfaces.

cs.RO↗

arXiv2025

100 Mfps ghost imaging with wavelength division multiplexing

Ghost imaging (GI) and single-pixel imaging (SPI) techniques enable image reconstruction without spatially resolved detectors, offering unique access to wide spectral ranges and challenging imaging environments. Yet, their adoption has been limited by the slow generation of mask patterns, which constrains achievable frame rates. Here, we demonstrate ultrafast GI that achieves a spatial-temporal information flux of 78.4 gigapixels per second across five wavelengths, which is at least two orders of magnitude larger than that reported for previous training-data-free GI approaches. This breakthrough is enabled by 25 GHz speckle pattern switching and allows parallelizing the pattern illumination using a wavelength-division multiplexing (WDM) technique. We show that the proposed approach is capable of reconstructing 28$\times$28-pixel images at the exposure time of 10 ns, achieving 100 megaframes per second (Mfps), and demonstrate the GI of a microsecond-scale dynamic event. This approach opens avenues for studying rapid processes in physics, chemistry, and biology, where conventional cameras are limited by detector bandwidth, readout speed, or cost.

physics.optics↗

arXiv2025

Expanding detection bandwidth via a photonic reservoir for ultrafast optical sensing

The detection of ultrafast optical and radio-frequency (RF) signals is crucial for applications ranging from high-speed communications to advanced sensing. However, conventional detectors are fundamentally constrained by their intrinsic bandwidth, limiting accurate broadband signal measurement. Here, we show that a neuromorphic photonic processing approach can overcome this limitation, enabling accurate broadband signal detection beyond the detector bandwidth. The key idea lies in the spatiotemporal encoding of input waveforms within a photonic reservoir network, which reconstructs high-frequency components otherwise inaccessible to individual detectors. We experimentally demonstrate the detection of high-speed optical phase signals with more than an eightfold effective bandwidth expansion using an on-chip silicon photonic reservoir. This approach provides a scalable and integrable platform for high-speed optical and RF signal processing, opening new opportunities in ultrafast photonics and next-generation communication systems.

physics.optics↗

arXiv2025

Programmable Photonic Circuit for Optical Logic Operations and 2-Bit Decoding

We present a programmable silicon photonic circuit composed of cascaded multiport directional couplers interleaved with thermo-optic phase shifters. The device forms a reconfigurable interferometric network capable of realizing arbitrary $N \times N$ unitary transformations. By tuning the phase shifters, the same circuit can be configured to perform both optical logic gates and 2-bit decoding functions. Optimal phase settings are determined through Bayesian optimization. The circuit maintains stable operation across multiple wavelengths with 50 GHz spacing, suggesting potential for wavelength-parallel optical processing. These results highlight a pathway toward programmable and integrated photonic circuits.

physics.optics↗

arXiv2025

Model-free front-to-end training of a large high performance laser neural network

Artificial neural networks (ANNs), have become ubiquitous and revolutionized many applications ranging from computer vision to medical diagnoses. However, they offer a fundamentally connectionist and distributed approach to computing, in stark contrast to classical computers that use the von Neumann architecture. This distinction has sparked renewed interest in developing unconventional hardware to support more efficient implementations of ANNs, rather than merely emulating them on traditional systems. Photonics stands out as a particularly promising platform, providing scalability, high speed, energy efficiency, and the ability for parallel information processing. However, fully realized autonomous optical neural networks (ONNs) with in-situ learning capabilities are still rare. In this work, we demonstrate a fully autonomous and parallel ONN using a multimode vertical cavity surface emitting laser (VCSEL) using off-the-shelf components. Our ONN is highly efficient and is scalable both in network size and inference bandwidth towards the GHz range. High performance hardware-compatible optimization algorithms are necessary in order to minimize reliance on external von Neumann computers to fully exploit the potential of ONNs. As such we present and extensively study several algorithms which are broadly compatible with a wide range of systems. We then apply these algorithms to optimize our ONN, and benchmark them using the MNIST dataset. We show that our ONN can achieve high accuracy and convergence efficiency, even under limited hardware resources. Crucially, we compare these different algorithms in terms of scaling and optimization efficiency in term of convergence time which is crucial when working with limited external resources. Our work provides some guidance for the design of future ONNs as well as a simple and flexible way to train them.

cs.LG↗

arXiv2025

Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks

The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical neural networks (PNNs) offer efficient neuromorphic information processing by harnessing the innate computational power of physical processes; however, training their weight parameters is computationally expensive. We propose a training approach for substantially reducing this training cost. Our training approach merges an optimal control method for continuous-time dynamical systems with a biologically plausible training method--direct feedback alignment. In addition to the reduction of training time, this approach achieves robust processing even under measurement errors and noise without requiring detailed system information. The effectiveness was numerically and experimentally verified in an optoelectronic delay system. Our approach significantly extends the range of physical systems practically usable as PNNs.

physics.app-ph↗

arXiv2024

Optical hyperdimensional soft sensing: Speckle-based touch interface and tactile sensor

Hyperdimensional computing (HDC) is an emerging computing paradigm that exploits the distributed representation of input data in a hyperdimensional space, the dimensions of which are typically between 1,000--10,000. The hyperdimensional distributed representation enables energy-efficient, low-latency, and noise-robust computations with low-precision and basic arithmetic operations. In this study, we propose optical hyperdimensional distributed representations based on laser speckles for adaptive, efficient, and low-latency optical sensor processing. In the proposed approach, sensory information is optically mapped into a hyperdimensional space with >250,000 dimensions, enabling HDC-based cognitive processing. We use this approach for the processing of a soft-touch interface and a tactile sensor and demonstrate to achieve high accuracy of touch or tactile recognition while significantly reducing training data amount and computational burdens, compared with previous machine-learning-based sensing approaches. Furthermore, we show that this approach enables adaptive recalibration to keep high accuracy even under different conditions.

eess.SP↗

arXiv2023

Ultrafast single-channel machine vision based on neuro-inspired photonic computing

High-speed machine vision is increasing its importance in both scientific and technological applications. Neuro-inspired photonic computing is a promising approach to speed-up machine vision processing with ultralow latency. However, the processing rate is fundamentally limited by the low frame rate of image sensors, typically operating at tens of hertz. Here, we propose an image-sensor-free machine vision framework, which optically processes real-world visual information with only a single input channel, based on a random temporal encoding technique. This approach allows for compressive acquisitions of visual information with a single channel at gigahertz rates, outperforming conventional approaches, and enables its direct photonic processing using a photonic reservoir computer in a time domain. We experimentally demonstrate that the proposed approach is capable of high-speed image recognition and anomaly detection, and furthermore, it can be used for high-speed imaging. The proposed approach is multipurpose and can be extended for a wide range of applications, including tracking, controlling, and capturing sub-nanosecond phenomena.

physics.optics↗

arXiv2023

Roadmap for Unconventional Computing with Nanotechnology

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

cs.ET↗

arXiv2022

Analysis of temporal structure of laser chaos by Allan variance

Allan variance has been widely utilized in evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This multi-scale examination capability of the Allan variance may also be beneficial in evaluating the chaotic oscillating dynamics of semiconductor lasers, not just for conventional phase stability analysis purposes. Here we demonstrate Allan variance analysis of the complex time series generated by a semiconductor laser with delayed feedback, including low-frequency fluctuations (LFFs), which exhibit both fast and slow dynamics. Whereas the detection of LFFs is not easy with the conventional power spectrum analysis method in the low-frequency regime, we show that the Allan variance approach clearly captured the appearance of multiple time-scale dynamics, like LFFs. This study demonstrates that Allan variance can help in understanding and characterizing versatile laser dynamics, including LFFs, which involve dynamics spanning a wide range of time scales.

physics.optics↗

arXiv2022

Gigahertz-rate random speckle projection for high-speed single-pixel image classification

Imaging techniques based on single-pixel detection, such as ghost imaging, can reconstruct or recognize a target scene from multiple measurements using a sequence of random mask patterns. However, the processing speed is limited by the low rate of the pattern generation. In this study, we propose an ultrafast method for random speckle pattern generation, which has the potential to overcome the limited processing speed. The proposed approach is based on multimode fiber speckles induced by fast optical phase modulation. We experimentally demonstrate dynamic speckle projection with phase modulation at 10 GHz rates, which is five to six orders of magnitude higher than conventional modulation approaches using spatial light modulators. Moreover, we combine the proposed generation approach with a wavelength-division multiplexing technique and apply it for image classification. As a proof-of-concept demonstration, we show that 28x28-pixel images of digits acquired at GHz rates can be accurately classified using a simple neural network. The proposed approach opens a novel pathway for an all-optical image processor.

physics.optics↗

arXiv2022

Parallel bandit architecture based on laser chaos for reinforcement learning

Accelerating artificial intelligence by photonics is an active field of study aiming to exploit the unique properties of photons. Reinforcement learning is an important branch of machine learning, and photonic decision-making principles have been demonstrated with respect to the multi-armed bandit problems. However, reinforcement learning could involve a massive number of states, unlike previously demonstrated bandit problems where the number of states is only one. Q-learning is a well-known approach in reinforcement learning that can deal with many states. The architecture of Q-learning, however, does not fit well photonic implementations due to its separation of update rule and the action selection. In this study, we organize a new architecture for multi-state reinforcement learning as a parallel array of bandit problems in order to benefit from photonic decision-makers, which we call parallel bandit architecture for reinforcement learning or PBRL in short. Taking a cart-pole balancing problem as an instance, we demonstrate that PBRL adapts to the environment in fewer time steps than Q-learning. Furthermore, PBRL yields faster adaptation when operated with a chaotic laser time series than the case with uniformly distributed pseudorandom numbers where the autocorrelation inherent in the laser chaos provides a positive effect. We also find that the variety of states that the system undergoes during the learning phase exhibits completely different properties between PBRL and Q-learning. The insights obtained through the present study are also beneficial for existing computing platforms, not just photonic realizations, in accelerating performances by the PBRL algorithms and correlated random sequences.

cs.ET↗

arXiv2022

Controlling chaotic itinerancy in laser dynamics for reinforcement learning

Photonic artificial intelligence has attracted considerable interest in accelerating machine learning; however, the unique optical properties have not been fully utilized for achieving higher-order functionalities. Chaotic itinerancy, with its spontaneous transient dynamics among multiple quasi-attractors, can be employed to realize brain-like functionalities. In this paper, we propose a method for controlling the chaotic itinerancy in a multi-mode semiconductor laser to solve a machine learning task, known as the multi-armed bandit problem, which is fundamental to reinforcement learning. The proposed method utilizes ultrafast chaotic itinerant motion in mode competition dynamics controlled via optical injection. We found that the exploration mechanism is completely different from a conventional searching algorithm and is highly scalable, outperforming the conventional approaches for large-scale bandit problems. This study paves the way to utilize chaotic itinerancy for effectively solving complex machine learning tasks as photonic hardware accelerators.

physics.optics↗

arXiv2022

Optical skin: Sensor-integration-free multimodal flexible sensing

The biological skin enables animals to sense various stimuli. Extensive efforts have been made recently to develop smart skin-like sensors to extend the capabilities of biological skins; however, simultaneous sensing of several types of stimuli in a large area remains challenging because this requires large-scale sensor integration with numerous wire connections. We propose a simple, highly sensitive, and multimodal sensing approach, which does not require integrating multiple sensors. The proposed approach is based on an optical interference technique, which can encode the information of various stimuli as a spatial pattern. In contrast to the existing approach, the proposed approach, combined with a deep neural network, enables us to freely select the sensing mode according to our purpose. As a key example, we demonstrate simultaneous sensing mode of three different physical quantities, contact force, contact location, and temperature, using a single soft material without requiring complex integration. Another unique property of the proposed approach is spatially continuous sensing with ultrahigh resolution of few tens of micrometers, which enables identifying the shape of the object in contact. Furthermore, we present a haptic soft device for a human-machine interface. The proposed approach encourages the development of high-performance optical skins.

cs.CV↗

arXiv2021

Power-law fluctuations near critical point in semiconductor lasers with delayed feedback

Since the analogy between laser oscillation and second-order phase transition was indicated in the 1970s, dynamical fluctuations on lasing threshold inherent in critical phenomena have gained significant interest. Here, we numerically and experimentally demonstrate that a semiconductor laser subject to delayed optical feedback can exhibit unusual large intensity fluctuations characterized by power-law distributions. Such an intensity fluctuation consists of distinct intermittent bursts of light intensity, whose peak values attain tens of times the intensity of the maximum gain mode. This burst behavior emerges when a laser with a long time delay (over 100 ns) and an optimal feedback strength operates around the lasing threshold. The intensity and waiting time statistics follow power-law-like distributions. This implies the emergence of nonequilibrium critical phenomena, namely self-organized criticality. In addition to numerical results, we report experimental results that suggest the power-law intensity dynamics in a semiconductor laser with delayed feedback.

physics.optics↗