由于其潜在的优势，如可扩展性，低延迟和功率效率，光学计算在过去几十年中已经看到了快速进步。潜在的全光处理器的核心单元将是NAND门，其可以级联以执行任意逻辑操作。在这里，我们使用衍射神经网络呈现可级可级联的全光NAND门的设计和分析。我们使用两个空间分离的孔的相对光功率在衍射NAND门的输入和输出平面上进行了编码的逻辑值。基于该架构，我们使用光的衍射来了数值优化了由4个无源层组成的衍射神经网络的设计，并通过光的衍射来实现这些衍射NAND操作，通过连续地馈送输出来级联这些衍射NAND门来执行复杂的逻辑功能一个衍射NAND门进入另一个。我们通过使用相同的衍射设计来显示我们的衍射NAND门的级联性，以及全光学执行和或操作以及半加法器。由空间工程化无源衍射层组成的可级可抵级光学NAND门可以用作各种光学计算平台的核心组件。

置换矩阵构成了一个重要的计算构建块，这些构建块在各个领域中经常使用，例如通信，信息安全和数据处理。具有相对较大数量的基于功率，快速和紧凑型平台的输入输出互连的置换运算符的光学实现是非常可取的。在这里，我们提出了通过深度学习设计的衍射光学网络，以全面执行置换操作，可以使用被动的传播层在输入和视场之间扩展到数十万个互连，这些互连是在波长规模上单独构造的。 。我们的发现表明，衍射光网络在近似给定置换操作中的容量与系统中衍射层和可训练的传输元件的数量成正比。这种更深的衍射网络设计可以在系统的物理对齐和输出衍射效率方面构成实际挑战。我们通过设计不对对准的衍射设计来解决这些挑战，这些设计可以全面执行任意选择的置换操作，并首次在实验中证明了在频谱的THZ部分运行的衍射排列网络。衍射排列网络可能会在例如安全性，图像加密和数据处理以及电信中找到各种应用程序；尤其是在无线通信中的载波频率接近THZ波段的情况下，提出的衍射置换网络可以潜在地充当无线网络中的通道路由和互连面板。

随机且未知的散射介质背后的对象的分类为计算成像和机器视野字段的具有挑战性的任务。最新的基于深度学习的方法证明了使用图像传感器收集的扩散器延伸模式对对象进行分类。这些方法需要使用在数字计算机上运行的深神经网络进行相对大规模的计算。在这里，我们提出了一个全光处理器，使用单个像素检测到的宽带照明通过未知的随机相扩散器直接对未知对象进行分类。使用深度学习进行了优化的一组传播衍射层，形成了一个物理网络，该物理网络全面地绘制了随机扩散器后面输入对象的空间信息，以进入通过单个像素在输出平面上检测到的输出光的功率谱，衍射网络。我们在数值上使用宽带辐射通过随机新扩散器对未知手写数字进行分类，在训练阶段从未使用过，并实现了88.53％的盲目测试准确性。这种通过随机扩散器的单像素全光对象分类系统基于被动衍射层，该层可以通过简单地缩放与波长范围的衍射范围来缩放衍射特征，从而在电磁光谱的任何部分中运行，并且可以在电磁光谱的任何部分工作。这些结果在例如生物医学成像，安全性，机器人技术和自动驾驶中具有各种潜在的应用。

波前调节器的限制空间散宽产品（SBP）阻碍了大型视野（FOV）上图像的高分辨率合成/投影。我们报告了一种深度学习的衍射显示设计，该设计基于一对训练的电子编码器和衍射光学解码器，用于合成/项目超级分辨图像，使用低分辨率波形调节器。由训练有素的卷积神经网络（CNN）组成的数字编码器迅速预处理了感兴趣的高分辨率图像，因此它们的空间信息被编码为低分辨率（LR）调制模式，该模式通过低SBP Wavefront调制器投影。衍射解码器使用薄的传播层处理该LR编码的信息，这些层是使用深度学习构成的，以在其输出FOV处进行全面合成和项目超级分辨图像。我们的结果表明，这种衍射图像显示可以达到〜4的超分辨率因子，表明SBP增加了约16倍。我们还使用3D打印的衍射解码器在THZ光谱上进行实验验证了这种衍射超分辨率显示器的成功。该衍射图像解码器可以缩放以在可见的波长下运行，并激发紧凑，低功率和计算效率的大型FOV和高分辨率显示器的设计。

Diffractive optical networks provide rich opportunities for visual computing tasks since the spatial information of a scene can be directly accessed by a diffractive processor without requiring any digital pre-processing steps. Here we present data class-specific transformations all-optically performed between the input and output fields-of-view (FOVs) of a diffractive network. The visual information of the objects is encoded into the amplitude (A), phase (P), or intensity (I) of the optical field at the input, which is all-optically processed by a data class-specific diffractive network. At the output, an image sensor-array directly measures the transformed patterns, all-optically encrypted using the transformation matrices pre-assigned to different data classes, i.e., a separate matrix for each data class. The original input images can be recovered by applying the correct decryption key (the inverse transformation) corresponding to the matching data class, while applying any other key will lead to loss of information. The class-specificity of these all-optical diffractive transformations creates opportunities where different keys can be distributed to different users; each user can only decode the acquired images of only one data class, serving multiple users in an all-optically encrypted manner. We numerically demonstrated all-optical class-specific transformations covering A-->A, I-->I, and P-->I transformations using various image datasets. We also experimentally validated the feasibility of this framework by fabricating a class-specific I-->I transformation diffractive network using two-photon polymerization and successfully tested it at 1550 nm wavelength. Data class-specific all-optical transformations provide a fast and energy-efficient method for image and data encryption, enhancing data security and privacy.

Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image field-of-view. This diffractive multispectral imager performs spatially-coherent imaging over a large spectrum, and at the same time, routes a pre-determined set of spectral channels onto an array of pixels at the output plane, converting a monochrome focal plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery algorithms. Furthermore, the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states. Through numerical simulations, we present different diffractive network designs that achieve snapshot multispectral imaging with 4, 9 and 16 unique spectral bands within the visible spectrum, based on passive spatially-structured diffractive surfaces, with a compact design that axially spans ~72 times the mean wavelength of the spectral band of interest. Moreover, we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially-repeating virtual spectral filter array with 2x2=4 unique bands at terahertz spectrum. Due to their compact form factor and computation-free, power-efficient and polarization-insensitive forward operation, diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available.

衍射深神经网络（D2NNS）定义了一个由空间工程的被动表面组成的全光计算框架，该框架通过调节传播光的幅度和/或相位来共同处理光学输入信息。衍射光学网络通过薄衍射量以光的速度来完成其计算任务，而无需任何外部计算能力，同时利用了光学的巨大并行性。证明了衍射网络以实现对象的全光分类并执行通用线性变换。在这里，我们首次证明了使用衍射网络的“延时”图像分类方案，通过使用输入对象的横向运动和/或衍射网络，可以显着提高其在复杂输入对象上的分类准确性和概括性性能。 ，相对于彼此。在不同的上下文中，通常将对象和/或相机的相对运动用于图像超分辨率应用程序；受其成功的启发，我们设计了一个延时衍射网络，以受益于由受控或随机横向移动创建的互补信息内容。我们从数值探索了延时衍射网络的设计空间和性能限制，从CIFAR-10数据集的对象进行光学分类中揭示了62.03％的盲测精度。这构成了迄今使用CIFAR-10数据集上的单个衍射网络达到的最高推理精度。延时衍射网络将对使用全光处理器的输入信号的时空分析广泛有用。

计算光学成像（COI）系统利用其设置中的光学编码元素（CE）在单个或多个快照中编码高维场景，并使用计算算法对其进行解码。 COI系统的性能很大程度上取决于其主要组件的设计：CE模式和用于执行给定任务的计算方法。常规方法依赖于随机模式或分析设计来设置CE的分布。但是，深神经网络（DNNS）的可用数据和算法功能已在CE数据驱动的设计中开辟了新的地平线，该设计共同考虑了光学编码器和计算解码器。具体而言，通过通过完全可区分的图像形成模型对COI测量进行建模，该模型考虑了基于物理的光及其与CES的相互作用，可以在端到端优化定义CE和计算解码器的参数和计算解码器（e2e）方式。此外，通过在同一框架中仅优化CE，可以从纯光学器件中执行推理任务。这项工作调查了CE数据驱动设计的最新进展，并提供了有关如何参数化不同光学元素以将其包括在E2E框架中的指南。由于E2E框架可以通过更改损耗功能和DNN来处理不同的推理应用程序，因此我们提出低级任务，例如光谱成像重建或高级任务，例如使用基于任务的光学光学体系结构来增强隐私的姿势估计，以维护姿势估算。最后，我们说明了使用全镜DNN以光速执行的分类和3D对象识别应用程序。

A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) A to an output FOV B, and in the reverse path, the image formation would be blocked. Here, we report the first demonstration of unidirectional imagers, presenting polarization-insensitive and broadband unidirectional imaging based on successive diffractive layers that are linear and isotropic. These diffractive layers are optimized using deep learning and consist of hundreds of thousands of diffractive phase features, which collectively modulate the incoming fields and project an intensity image of the input onto an output FOV, while blocking the image formation in the reverse direction. After their deep learning-based training, the resulting diffractive layers are fabricated to form a unidirectional imager. As a reciprocal device, the diffractive unidirectional imager has asymmetric mode processing capabilities in the forward and backward directions, where the optical modes from B to A are selectively guided/scattered to miss the output FOV, whereas for the forward direction such modal losses are minimized, yielding an ideal imaging system between the input and output FOVs. Although trained using monochromatic illumination, the diffractive unidirectional imager maintains its functionality over a large spectral band and works under broadband illumination. We experimentally validated this unidirectional imager using terahertz radiation, very well matching our numerical results. Using the same deep learning-based design strategy, we also created a wavelength-selective unidirectional imager, where two unidirectional imaging operations, in reverse directions, are multiplexed through different illumination wavelengths. Diffractive unidirectional imaging using structured materials will have numerous applications in e.g., security, defense, telecommunications and privacy protection.

Photonic neural networks are brain-inspired information processing technology using photons instead of electrons to perform artificial intelligence (AI) tasks. However, existing architectures are designed for a single task but fail to multiplex different tasks in parallel within a single monolithic system due to the task competition that deteriorates the model performance. This paper proposes a novel optical multi-task learning system by designing multi-wavelength diffractive deep neural networks (D2NNs) with the joint optimization method. By encoding multi-task inputs into multi-wavelength channels, the system can increase the computing throughput and significantly alle-viate the competition to perform multiple tasks in parallel with high accuracy. We design the two-task and four-task D2NNs with two and four spectral channels, respectively, for classifying different inputs from MNIST, FMNIST, KMNIST, and EMNIST databases. The numerical evaluations demonstrate that, under the same network size, mul-ti-wavelength D2NNs achieve significantly higher classification accuracies for multi-task learning than single-wavelength D2NNs. Furthermore, by increasing the network size, the multi-wavelength D2NNs for simultaneously performing multiple tasks achieve comparable classification accuracies with respect to the individual training of multiple single-wavelength D2NNs to perform tasks separately. Our work paves the way for developing the wave-length-division multiplexing technology to achieve high-throughput neuromorphic photonic computing and more general AI systems to perform multiple tasks in parallel.

与常规深层神经网络（DNN）相比，衍射光学神经网络（DONNS）在功率效率，并行性和计算速度方面具有显着优势，因此引起了很多关注，这些神经网络（DNN）在数字平台上实现时具有内在的限制。但是，反相反的算法训练的物理模型参数上具有离散值的现实世界光学设备是一个非平凡的任务，因为现有的光学设备具有非统一的离散级别和非单调属性。这项工作提出了一个新颖的设备对系统硬件软件代码框架，该框架可以对Donns W.R.T的有效物理意识培训进行跨层的任意实验测量的光学设备。具体而言，使用Gumbel-SoftMax来启用从现实世界设备参数的可区分映射到Donns的正向函数，在Donn中，Donn中的物理参数可以通过简单地最小化ML任务的损耗函数来训练。结果表明，我们提出的框架比传统的基于基于量化的方法具有显着优势，尤其是使用低精确的光学设备。最后，在低精度设置中，通过物理实验光学系统对所提出的算法进行了充分的验证。

The ultimate goal of artificial intelligence is to mimic the human brain to perform decision-making and control directly from high-dimensional sensory input. All-optical diffractive neural networks provide a promising solution for realizing artificial intelligence with high-speed and low-power consumption. To date, most of the reported diffractive neural networks focus on single or multiple tasks that do not involve interaction with the environment, such as object recognition and image classification, while the networks that can perform decision-making and control, to our knowledge, have not been developed yet. Here, we propose to use deep reinforcement learning to realize diffractive neural networks that enable imitating the human-level capability of decision-making and control. Such networks allow for finding optimal control policies through interaction with the environment and can be readily realized with the dielectric metasurfaces. The superior performances of these networks are verified by engaging three types of classic games, Tic-Tac-Toe, Super Mario Bros., and Car Racing, and achieving the same or even higher levels comparable to human players. Our work represents a solid step of advancement in diffractive neural networks, which promises a fundamental shift from the target-driven control of a pre-designed state for simple recognition or classification tasks to the high-level sensory capability of artificial intelligence. It may find exciting applications in autonomous driving, intelligent robots, and intelligent manufacturing.

神经形态工程由于其作为研究领域的巨大潜力而​​集中了大量研究人员的努力，以寻找对生物神经系统的优势的利用，而整个大脑的优势是设计更有效，更真实的 - 有能力的应用程序。为了开发尽可能接近生物学的应用，使用了尖峰神经网络（SNN），被认为是生物学上的，并构成了第三代人工神经网络（ANN）。由于某些基于SNN的应用程序可能需要存储数据才能以后使用，因此在数字电路中既存在，又以某种形式，在生物学中，需要尖峰内存。这项工作介绍了内存的尖峰实现，这是计算机架构中最重要的组件之一，在设计完全尖峰计算机时可能至关重要。在设计这种尖峰内存的过程中，还实施了不同的中间组件和测试。测试是在大三角帆神经形态平台上进行的，并允许验证用于构建所构图的方法。此外，这项工作深入研究了如何使用这种方法构建尖峰块，并包括IT和其他类似作品中使用的方法的比较，该作品着重于尖峰组件的设计，其中包括尖峰逻辑门和尖峰记忆。所有实施的块和开发的测试均可在公共存储库中提供。

The ever-growing deep learning technologies are making revolutionary changes for modern life. However, conventional computing architectures are designed to process sequential and digital programs, being extremely burdened with performing massive parallel and adaptive deep learning applications. Photonic integrated circuits provide an efficient approach to mitigate bandwidth limitations and power-wall brought by its electronic counterparts, showing great potential in ultrafast and energy-free high-performance computing. Here, we propose an optical computing architecture enabled by on-chip diffraction to implement convolutional acceleration, termed optical convolution unit (OCU). We demonstrate that any real-valued convolution kernels can be exploited by OCU with a prominent computational throughput boosting via the concept of structral re-parameterization. With OCU as the fundamental unit, we build an optical convolutional neural network (oCNN) to implement two popular deep learning tasks: classification and regression. For classification, Fashion-MNIST and CIFAR-4 datasets are tested with accuracy of 91.63% and 86.25%, respectively. For regression, we build an optical denoising convolutional neural network (oDnCNN) to handle Gaussian noise in gray scale images with noise level {\sigma} = 10, 15, 20, resulting clean images with average PSNR of 31.70dB, 29.39dB and 27.72dB, respectively. The proposed OCU presents remarkable performance of low energy consumption and high information density due to its fully passive nature and compact footprint, providing a highly parallel while lightweight solution for future computing architecture to handle high dimensional tensors in deep learning.

量子计算是使用量子力学执行计算的过程。该领域研究某些亚杀菌粒子的量子行为，以便随后在执行计算，以及大规模信息处理中使用。这些能力可以在计算时间和经典计算机上的成本方面提供量子计算机的优势。如今，由于计算复杂性或计算所需的时间，具有科学挑战，这是由于古典计算而无法执行，并且量子计算是可能的答案之一。然而，电流量子器件尚未实现必要的QUBITS，并且没有足够的容错才能实现这些目标。尽管如此，还有其他领域，如机器学习或化学，其中量子计算对电流量子器件有用。本手稿旨在展示2017年和2021年之间发布的论文的系统文献综述，以确定，分析和分类量子机器学习和其应用中使用的不同算法。因此，该研究确定了使用量子机器学习技术和算法的52篇文章。发现算法的主要类型是经典机器学习算法的量子实现，例如支持向量机或K最近邻模型，以及古典的深度学习算法，如量子神经网络。许多文章试图解决目前通过古典机器学习回答的问题，但使用量子设备和算法。即使结果很有希望，量子机器学习也远未实现其全部潜力。由于现有量子计算机缺乏足够的质量，速度和比例以允许量子计算来实现其全部潜力，因此需要提高量子硬件。

Ever since the first microscope by Zacharias Janssen in the late 16th century, scientists have been inventing new types of microscopes for various tasks. Inventing a novel architecture demands years, if not decades, worth of scientific experience and creativity. In this work, we introduce Differentiable Microscopy ($\partial\mu$), a deep learning-based design paradigm, to aid scientists design new interpretable microscope architectures. Differentiable microscopy first models a common physics-based optical system however with trainable optical elements at key locations on the optical path. Using pre-acquired data, we then train the model end-to-end for a task of interest. The learnt design proposal can then be simplified by interpreting the learnt optical elements. As a first demonstration, based on the optical 4-$f$ system, we present an all-optical quantitative phase microscope (QPM) design that requires no computational post-reconstruction. A follow-up literature survey suggested that the learnt architecture is similar to the generalized phase contrast method developed two decades ago. Our extensive experiments on multiple datasets that include biological samples show that our learnt all-optical QPM designs consistently outperform existing methods. We experimentally verify the functionality of the optical 4-$f$ system based QPM design using a spatial light modulator. Furthermore, we also demonstrate that similar results can be achieved by an uninterpretable learning based method, namely diffractive deep neural networks (D2NN). The proposed differentiable microscopy framework supplements the creative process of designing new optical systems and would perhaps lead to unconventional but better optical designs.

Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever-increasing complexity has resulted in exponential growth in computation cost, leading to long simulation time and high energy consumption. Photonic chip technology offers an alternative platform for implementing neural networks with faster data processing and lower energy usage compared to digital computers. Photonics technology is naturally capable of implementing complex-valued neural networks at no additional hardware cost. Here, we demonstrate the capability of photonic neural networks for predicting the quantum mechanical properties of molecules. To the best of our knowledge, this work is the first to harness photonic technology for machine learning applications in computational chemistry and molecular sciences, such as drug discovery and materials design. We further show that multiple properties can be learned simultaneously in a photonic chip via a multi-task regression learning algorithm, which is also the first of its kind as well, as most previous works focus on implementing a network in the classification task.

在这个接近中间尺度的量子时代，云上有两种类型的近期量子设备：基于离散变量模型和线性光学器件（Photonics）QPU的超导量子处理单元（QPU），基于连续变量（CV）） 模型。离散变量模型中的量子计算以有限的尺寸量子状态空间和无限尺寸空间中的CV模型执行。在实现量子算法时，CV模型提供了更多的量子门，这些量子门在离散变量模型中不可用。基于简历的光子量子计算机使用不同的测量方法和截止尺寸的概念来控制量子电路的输出向量长度的额外灵活性。

与神经网络的软件模拟相反，硬件实现通常有限或没有可调性。尽管此类网络在速度和能源效率方面有了很大的改善，但它们的性能受到应用有效培训的困难的限制。我们建议并实现实验性的光学系统，在该系统中，可以通过一系列高度非线性的，不可调节的节点来应用高效的反向传播训练。该系统包括实现非线性激活函数的激子孔节点。我们在单个隐藏层系统中的MNIST手写数字基准中演示了高分类精度。

在这项工作中，我们介绍了一种光电尖峰，能够以超速率（$ \ \左右100磅/光学尖峰）和低能耗（$ <$ PJ /秒码）运行。所提出的系统结合了具有负差分电导的可激发谐振隧道二极管（RTD）元件，耦合到纳米级光源（形成主节点）或光电探测器（形成接收器节点）。我们在数值上学习互连的主接收器RTD节点系统的尖峰动态响应和信息传播功能。使用脉冲阈值和集成的关键功能，我们利用单个节点来对顺序脉冲模式进行分类，并对图像特征（边缘）识别执行卷积功能。我们还展示了光学互连的尖峰神经网络模型，用于处理超过10 Gbps的时空数据，具有高推理精度。最后，我们展示了利用峰值定时依赖性可塑性的片外监督的学习方法，使能RTD的光子尖峰神经网络。这些结果证明了RTD尖峰节点用于低占地面积，低能量，高速光电实现神经形态硬件的潜在和可行性。