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neural network models for optical computing 1988

The proceedings contain 24 papers. The topics discussed include: optically connected multiprocessors for simulating artificial neural networks;hardware implementation of an artificial neural network;optical neural network models applied to logic program execution;adaptive learning optical symbolic processor;continuous time neural networks;optical associative processors for visual perception;new recording algorithm for Hopfield model associative memories;holographic implementation of a discrete Hopfield neural network model;improving the performance of neural networks;optical neural classification of binary patterns;and architectures for a continuous level neural network based on alternating orthogonal projections.

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neural network models for optical computing

Neural network models for optical computing

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1988年

作者： Athale

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Quantum optical Convolutional neural network: A Novel Image Recognition Framework for Quantum computing

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IEEE ACCESS 2021年 9卷 103337-103346页

作者： Parthasarathy, Rishab Bhowmik, Rohan T. Harker Sch San Jose CA 95129 USA

Large machine learning models based on Convolutional neural networks (CNNs) with rapidly increasing numbers of parameters are being deployed in a wide array of computer vision tasks. However, the insatiable demand for computing resources required to train these models is fast outpacing the advancement of classical computing hardware, and new frameworks including optical neural networks (ONNs) and quantum computing are being explored as future alternatives. In this work, we report a novel quantum computing based deep learning model, the Quantum optical Convolutional neural network (QOCNN), to alleviate the computational bottleneck in future computer vision applications. Using the MNIST dataset, we have benchmarked this new architecture against a traditional CNN model based on the seminal LeNet architecture. We have also compared the performance against previously reported ONNs, the GridNet and ComplexNet, and a Quantum optical neural network (QONN) that we built by combining the ComplexNet with quantum-inspired sinusoidal nonlinearities. Our work extends the prior research on QONN by adding quantum convolution and pooling layers. We have evaluated the models using the metrics of accuracies, confusion matrices, Receiver Operating Characteristic curves, and Matthews Correlation Coefficients. The performance of the models were similar, and the metrics indicated that the new QOCNN model is robust. Finally, we estimated the gains in computational efficiencies from executing this novel framework on a quantum computer, concluding that switching to a quantum computing based approach to deep learning may result in comparable accuracies to classical models while simultaneously achieving unprecedented boosts in computational performances and power allocation.

关键词： Quantum computing Computational modeling Artificial intelligence optical imaging optical network units optical computing Convolutional neural networks Deep learning computer vision image recognition convolutional neural networks quantum computing quantum photonics

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High-Efficiency Reconfigurable optical Convolutional neural network

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第9期43卷 4076-4085页

作者： Li, Junshen Meng, Xiangyan Zhang, Jingyun Li, Wei Shi, Nuannuan Li, Ming Chinese Acad Sci Inst Semicond State Key Lab Optoelect Mat & Devices Beijing 100083 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Mat Sci & Optoelect Technol Beijing 100190 Peoples R China Tencent WeChat Pay Lab33 Shenzhen 518054 Peoples R China

As the intricacy of network models escalates and the volume of data swells exponentially, the demands on the processing capabilities of neural networks have become increasingly rigorous. optical computing, with the excellent performance of extremely large bandwidth and ultra-low loss, is poised to be a disruptive technology that supports the needs of the next generation high performance artificial intelligence. In this paper, an optical convolutional neural network, combining a novel architectural design with a compatible data encoding technique, is introduced to achieve superior data efficiency by harnessing the dimensions of wavelength, temporal and parallel space. Utilizing 3x3 convolution kernel on 28x28-pixel images, the proportion of valid data in the output temporal waveform achieves as high as 92.86%. Furthermore, by employing multi-kernel parallel for processing larger size image, the data efficiency is ulteriorly enhanced to an astonishing 99%, and the potential computing power is increased to 10.51 TOPS, showcasing a remarkable leap in performance. During the experiment, five reconfigurable kernels including two positive and three real-value kernels, in conjunction with an electrical fully connected layer, successfully classified handwritten digital images from "0" to "9" in Modified National Institute of Standards and Technology (MNIST) database, achieving an accuracy of 92.81%. The defining feature of the proposed structure is that high-efficiency reconfigurable optical convolutional neural networks streamline the preprocessing and extraction of the data, markedly reducing the meaninglessdata ratio. This innovation, offering a new architecture for optical convolutional neural networks, not only conserves computing power but also improve processing speed, which is expected to alleviate the expanding demand for computing performance in deep learning.

关键词： optical imaging optical computing optical fibers Convolutional neural networks Convolution optical fiber networks Kernel optical network units optical crosstalk Encoding deep learning optical computing optical neural network

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Intelligent Photonics:A Disruptive Technology to Shape the Present and Redefine the Future

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Engineering 2025年第3期46卷 186-213页

作者： Danlin Xu Yuchen Ma Guofan Jin Liangcai Cao State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentsTsinghua UniversityBeijing 100084China

Artificial intelligence(AI)has taken breathtaking leaps forward in recent years,evolving into a strategic technology for pioneering the *** growing demand for computing power—especially in demanding inference tasks,exemplified by generative AI models such as ChatGPT—poses challenges for conventional electronic computing *** in photonics technology have ignited interest in investigating photonic computing as a promising AI computing *** the profound fusion of AI and photonics technologies,intelligent photonics is developing as an emerging interdisciplinary field with significant potential to revolutionize practical *** learning,as a subset of AI,presents efficient avenues for optimizing photonic design,developing intelligent optical systems,and performing optical data processing and *** AI in photonics can empower applications such as smartphone cameras,biomedical microscopy,and virtual and augmented reality ***,leveraging photonics-based devices and systems for the physical implementation of neural networks enables high speed and low energy *** photonics technology in AI computing is expected to have a transformative impact on diverse fields,including optical communications,automatic driving,and astronomical ***,recent advances in intelligent photonics are presented from the perspective of the synergy between deep learning and metaphotonics,holography,and quantum *** review also spotlights relevant applications and offers insights into challenges and prospects.

关键词： Artificial intelligence optical neural network Deep learning Metaphotonics Holography Quantum photonics

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Confluence of Photonics and Artificial Intelligence

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 2025年第3期31卷

作者： Chen, Young-Kai Coherent Corp Santa Clara CA 95054 USA

Over the past decades, significant advances have been made in the fields of photonic technologies, artificial intelligence, and machine learning techniques. Recent AI progress in language models, perception and self-learning capability has had a significant impact on a wide range of communities. The new photonics enhances AI with faster data processing, reduced energy consumption, and improved interconnects, enabling more efficient and powerful AI systems. In this paper, we will highlight the synchroneity of these advances from performance enhancement of AI processing by photonics and advanced photonics with AI processing, leading to new photonic neural networks for future photonic AI processors.

关键词： optical switches Artificial intelligence High-speed optical techniques optical fiber communication optical distortion optical interconnections Photonics optical amplifiers optical polarization Training optical interconnects optical switching photonic inverse design photonic processing photonic neural network photonic computing artificial intelligence machine learning

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Multi-Physical Intelligent Analysis of the Thermal-Structural-optical Coupling for Mirror Based on Surrogate Model

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IEEE ACCESS 2025年 13卷 14113-14121页

作者： Li, Wenxiong Shen, Junli Lu, Zhenyu Men, Shudong Wu, Qingwen Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Jilin Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Key Lab Onorbit Mfg & Integrat Space Opt Syst Changchun 130033 Jilin Peoples R China

The thermal-structural-optical multi-physical coupling analysis (TSO-MCA) of infrared optical systems and their mirrors during the establishment of a cryogenic environment is a complex multi-physical coupling problem involving mechanics, thermal, optics and other factors. Currently, the finite element method's workflow is complex and cumbersome, and it cannot simulate the continuous process of establishing a cryogenic environment. Therefore, a surrogate modeling approach based on a neural network for the TSO-MCA of mirrors has been proposed to analyze the surface shape of optical system mirrors under the action of multi-physical fields. Firstly, a thermal-structural-optical multi-physical integrated chain(TSO-IC) is established by integrating various disciplinary simulation and analysis tools, through which computational data necessary for the surrogate model are acquired. Secondly, a hybrid network combining Transformer and Long Short-Term Memory (LSTM) networks is proposed. The high-precision TSO-MCA surrogate model is obtained through training and K-fold cross validation. Compared with traditional methods, the hybrid network has the smallest symmetric mean absolute percentage error (SMAPE) of 2.674% on the same test set, which indicates that the hybrid network obtains a higher prediction accuracy of the surrogate model. Finally, through the thermal structure-optical coupling analysis of a mirror of an optical system, it is shown that the high precision mirror TSO-MCA based on the surrogate model is 3 orders of magnitude faster than the traditional finite element analysis in terms of computation time, indicating that the surrogate model is more efficient and convenient.

关键词： Mirrors Computational modeling neural networks Analytical models Couplings Thermal analysis optical computing Numerical models Adaptive optics Temperature distribution Thermal-structural-optical coupled mirror surrogate model multi-physics neural network

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Monolithically integrated asynchronous optical recurrent accelerator

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ELIGHT 2025年第1期5卷 1-14页

作者： Wu, Bo Zhou, Haojun Cheng, Junwei Zhang, Wenkai Zhang, Shiji Huang, Chaoran Huang, Dongmei Zhou, Hailong Dong, Jianji Zhang, Xinliang Huazhong Univ Sci & Technol Sch Opt & Elect Informat Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China Hong Kong Polytech Univ Photon Res Inst Dept Elect & Elect Engn Hong Kong Peoples R China Xidian Univ Xian 710071 Peoples R China

computing with light is widely recognized as a promising paradigm for overcoming the energy and latency limitations of electronic computing. However, the energy consumption and latency in current optical computing hardware predominantly arise in the electrical domain rather than the optical domain, primarily due to frequent signal conversions between optical (analog) and electrical (digital) formats. Furthermore, as the operating frequency of optical computing surpasses the GHz range, the synchronization of parallel electrical signals and the management of optical delays become increasingly critical. These challenges exacerbate energy consumption and latency, particularly in recurrent optical operations. To address these limitations, we propose a novel asynchronous computing paradigm for on-chip optical recurrent accelerators based on wavelength encoding, effectively mitigating synchronization challenges. By leveraging the intrinsic causality of wavelength relay, our approach eliminates the need for rigorous temporal alignment. To demonstrate the flexibility and efficacy of this asynchronous paradigm, we present two advanced recurrent models-an optical hidden Markov model and an optical recurrent neural network-monolithically integrated for the first time. These models incorporate hundreds of linear and nonlinear computing units densely packed into a compact footprint of just 10 mm2. Experimental evaluations on various benchmark tasks underscore the superior energy efficiency and low latency of the proposed asynchronous optical accelerators. This innovation enables the efficient processing of large-scale parallel signals and positions optical processors as a pivotal technology for applications such as autonomous driving and intelligent robotics.

关键词： Asynchronous operation optical recurrent accelerator optical hidden Markov model optical recurrent neural network

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Quantum neural network Boosting Identifying Orbital Angular Momentum Modes

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ADVANCED QUANTUM TECHNOLOGIES 2025年第0期

作者： Merabet, Badreddine Li, Liulin Shah, Syed Afaq Ali Guo, Zhongyi Hefei Univ Technol Sch Comp & Informat Engn Hefei 230000 Peoples R China

Quantum machine learning (QML) offers a breakthrough by producing atypical patterns that classical systems cannot efficiently produce. This study explores quantum neural networks (QNN) for classifying orbital angular momentum (OAM) modes. A fully quantum model achieving 99.8% accuracy across 10 OAM classes and a hybrid ConvQuantum model reaching accuracy levels of 99.8% under atmospheric turbulence strength (AT) of Cn2=1x10-14m-2/3$C_n<^>2 = 1 \times {{10}<^>{-14}}{{m}<^>{-2/3}}$ is developed. The ConvQuantum model significantly reduces prediction time, averaging 0.00025 s per sample, which is essential for real-time free space optical (FSO) communication. To demonstrate quantum technology's potential in OAM-FSO communication, a 100 x 100 grayscale image is successfully transmitted, and compared the results with several known models in the field, including feedforward neural networks (FNN), convolutional neural network (CNN), and a vision transformer (ViT) model. The findings indicate that the QNN models outperform classical approaches in calculation time, showcasing the efficiency and enhanced processing power of quantum computing in OAM-FSO communication.

关键词： free-space optical (FSO) communication orbital angular momentum (OAM) quantum machine learning (QML) quantum neural networks (QNN)

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All-optical perception based on partially coherent optical neural networks

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OPTICS EXPRESS 2025年第2期33卷 1609-1624页

作者： Chen, Rui Ma, Yijun Zhang, Chuang Xu, Wenjun Wang, Zhong Sun, Shengli Chinese Acad Sci Shanghai Inst Tech Phys Shanghai 200083 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Intelligent Infrared Percept Shanghai 200083 Peoples R China

In the field of image processing, optical neural networks offer advantages such as high speed, high throughput, and low energy consumption. However, most existing coherent optical neural networks (CONN) rely on coherent light sources to establish transmission models. The use of laser inputs and electro-optic modulation devices at the front end of these neural networks diminishes their computational capability and energy efficiency, thereby limiting their practical applications in object detection tasks. This paper proposes a partially coherent optical neural network (PCONN) transmission model based on mutual intensity modulation. This model does not depend on coherent light source inputs or active electro-optic modulation devices, allowing it to directly compute and infer using natural light after simple filtering, thus achieving full optical perception from light signal acquisition to computation and inference. Simulation results indicate that the model achieves a highest classification accuracy of 96.80% and 86.77% on the MNIST and Fashion-MNIST datasets, respectively. In a binary classification simulation test based on the ISDD segmentation dataset, the model attained an accuracy of 94.69%. It is estimated that this system's computational inference speed for object detection tasks is 100 times faster than that of traditional CONN, with energy efficiency approximately 50 times greater. In summary, our proposed PCONN model addresses the limitations of conventional optical neural networks in coherent light environments and is anticipated to find applications in practical object detection scenarios. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： High throughput optics Huygens Fresnel principle Information processing optical computing optical neural systems Point spread function

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