Reconstruction algorithm for cross-waveband optical computing imaging

In a single-pixel fast imaging setup,the data collected by the single-pixel detector needs to be processed by a computer,but the speed of the latter will affect the image reconstruction time.Here we propose two kinds of setups which are able to transform non-visible into visible light imaging,wherein their computing process is replaced by a camera integration mode.The image captured by the camera has a low contrast,so here we present an algorithm that can realize a high quality image in near-infrared to visible cross-waveband imaging.The scheme is verified both by simulation and in actual experiments.The setups demonstrate the great potential for single-pixel imaging and high-speed cross-waveband imaging for future practical applications.

All-optical object identification and threedimensional reconstruction based on optical computing metasurface

Object identification and three-dimensional reconstruction techniques are always attractive research interests in machine vision,virtual reality,augmented reality,and biomedical engineering.Optical computing metasurface,as a two-dimensional artificial design component,has displayed the supernormal character of controlling phase,amplitude,polarization,and frequency distributions of the light beam,capable of performing mathematical operations on the input light field.Here,we propose and demonstrate an all-optical object identification technique based on optical computing metasurface,and apply it to 3D reconstruction.Unlike traditional mechanisms,this scheme reduces memory consumption in the processing of the contour surface extraction.The identification and reconstruction of experimental results from high-contrast and low-contrast objects agree well with the real objects.The exploration of the all-optical object identification and 3D reconstruction techniques provides potential applications of high efficiencies,low consumption,and compact systems.

Research on the model of high robustness computational optical imaging system

Computational optical imaging is an interdisciplinary subject integrating optics, mathematics, and information technology. It introduces information processing into optical imaging and combines it with intelligent computing, subverting the imaging mechanism of traditional optical imaging which only relies on orderly information transmission. To meet the high-precision requirements of traditional optical imaging for optical processing and adjustment, as well as to solve its problems of being sensitive to gravity and temperature in use, we establish an optical imaging system model from the perspective of computational optical imaging and studies how to design and solve the imaging consistency problem of optical system under the influence of gravity, thermal effect, stress, and other external environment to build a high robustness optical system. The results show that the high robustness interval of the optical system exists and can effectively reduce the sensitivity of the optical system to the disturbance of each link, thus realizing the high robustness of optical imaging.

Multifunctional mixed analog/digital signal processor based on integrated photonics

Photonic signal processing offers a versatile and promising toolkit for contemporary scenarios ranging from digital optical communication to analog microwave operation.Compared to its electronic counterpart,it eliminates inherent bandwidth limitations and meanwhile exhibits the potential to provide unparalleled scalability and flexibility,particularly through integrated photonics.However,by far the on-chip solutions for optical signal processing are often tailored to specific tasks,which lacks versatility across diverse applications.Here,we propose a streamlined chip-level signal processing architecture that integrates different active and passive building blocks in silicon-on-insulator(SOI)platform with a compact and efficient manner.Comprehensive and in-depth analyses for the architecture are conducted at levels of device,system,and application.Accompanied by appropriate configuring schemes,the photonic circuitry supports loading and processing both analog and digital signals simultaneously.Three distinct tasks are facilitated with one single chip across several mainstream fields,spanning optical computing,microwave photonics,and optical communications.Notably,it has demonstrated competitive performance in functions like image processing,spectrum filtering,and electro-optical bandwidth equalization.Boasting high universality and a compact form factor,the proposed architecture is poised to be instrumental for next-generation functional fusion systems.

A review:Photonics devices,architectures,and algorithms for optical neural computing

The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era.Photonics neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed,wide bandwidth,and massive parallelism.Here,we offer a review on the optical neural computing in our research groups at the device and system levels.The photonics neuron and photonics synapse plasticity are presented.In addition,we introduce several optical neural computing architectures and algorithms including photonic spiking neural network,photonic convolutional neural network,photonic matrix computation,photonic reservoir computing,and photonic reinforcement learning.Finally,we summarize the major challenges faced by photonic neuromorphic computing,and propose promising solutions and perspectives.

Analog Optical Computing for Artificial Intelligence

The rapid development of artificial intelligence(AI)facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data.Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth,low latency,and high energy efficiency.In this review,we introduce the latest developments of optical computing for different AI models,including feedforward neural networks,reservoir computing,and spiking neural networks(SNNs).Recent progress in integrated photonic devices,combined with the rise of AI,provides a great opportunity for the renaissance of optical computing in practical applications.This effort requires multidisciplinary efforts from a broad community.This review provides an overview of the state-of-the-art accomplishments in recent years,discusses the availability of current technologies,and points out various remaining challenges in different aspects to push the frontier.We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks.

All-optical logic gate computing for high-speed parallel information processing

Optical computing and optical neural network have gained increasing attention in recent years because of their potential advantages of parallel processing at the speed of light and low power consumption by comparison with electronic computing.The optical implementation of the fundamental building blocks of a digital computer,i.e.logic gates,has been investigated extensively in the past few decades.Optical logic gate computing is an alternative approach to various analogue optical computing architectures.In this paper,the latest development of optical logic gate computing with different kinds of implementations is reviewed.Firstly,the basic concepts of analogue and digital computing with logic gates in the electronic and optical domains are introduced.And then a comprehensive summary of various optical logic gate schemes including spatial encoding of light field,semiconductor optical amplifiers(SOA),highly nonlinear fiber(HNLF),microscale and nanoscale waveguides,and photonic crystal structures is presented.To conclude,the formidable challenges in developing practical all-optical logic gates are analyzed and the prospects of the future are discussed.

A Novel Stateful PCE-Cloud Based Control Architecture of Optical Networks for Cloud Services

The next-generation optical network is a service oriented network,which could be delivered by utilizing the generalized multiprotocol label switching(GMPLS) based control plane to realize lots of intelligent features such as rapid provisioning,automated protection and restoration(P&R),efficient resource allocation,and support for different quality of service(QoS) requirements.In this paper,we propose a novel stateful PCE-cloud(SPC)based architecture of GMPLS optical networks for cloud services.The cloud computing technologies(e.g.virtualization and parallel computing) are applied to the construction of SPC for improving the reliability and maximizing resource utilization.The functions of SPC and GMPLS based control plane are expanded according to the features of cloud services for different QoS requirements.The architecture and detailed description of the components of SPC are provided.Different potential cooperation relationships between public stateful PCE cloud(PSPC) and region stateful PCE cloud(RSPC) are investigated.Moreover,we present the policy-enabled and constraint-based routing scheme base on the cooperation of PSPC and RSPC.Simulation results for verifying the performance of routing and control plane reliability are analyzed.

Multi-target ranging using an optical reservoir computing approach in the laterally coupled semiconductor lasers with self-feedback

We utilize three parallel reservoir computers using semiconductor lasers with optical feedback and light injection to model radar probe signals with delays.Three radar probe signals are generated by driving lasers constructed by a threeelement laser array with self-feedback.The response lasers are implemented also by a three-element lase array with both delay-time feedback and optical injection,which are utilized as nonlinear nodes to realize the reservoirs.We show that each delayed radar probe signal can be predicted well and to synchronize with its corresponding trained reservoir,even when parameter mismatches exist between the response laser array and the driving laser array.Based on this,the three synchronous probe signals are utilized for ranging to three targets,respectively,using Hilbert transform.It is demonstrated that the relative errors for ranging can be very small and less than 0.6%.Our findings show that optical reservoir computing provides an effective way for applications of target ranging.

Ferroelectric Liquid Crystal Gates and Optical Computing

The surface stabilized ferroelectric liquid crystal device configuration and ferroelectric liquid crystal gates are described.The liquid crystal electrooptical gates have numerous applications,including optical computation,optodigital circuits,and optical communication networks.

Pattern recognition in multi-synaptic photonic spiking neural networks based on a DFB-SA chip

Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuromorphic computing.Here,we proposed a multi-synaptic photonic SNN,combining the modified remote supervised learning with delayweight co-training to achieve pattern classification.The impact of multi-synaptic connections and the robustness of the network were investigated through numerical simulations.In addition,the collaborative computing of algorithm and hardware was demonstrated based on a fabricated integrated distributed feedback laser with a saturable absorber(DFB-SA),where 10 different noisy digital patterns were successfully classified.A functional photonic SNN that far exceeds the scale limit of hardware integration was achieved based on time-division multiplexing,demonstrating the capability of hardware-algorithm co-computation.

Diffractive Deep Neural Networks at Visible Wavelengths

Optical deep learning based on diffractive optical elements offers unique advantages for parallel processing,computational speed,and power efficiency.One landmark method is the diffractive deep neural network(D^(2) NN)based on three-dimensional printing technology operated in the terahertz spectral range.Since the terahertz bandwidth involves limited interparticle coupling and material losses,this paper extends D^(2) NN to visible wavelengths.A general theory including a revised formula is proposed to solve any contradictions between wavelength,neuron size,and fabrication limitations.A novel visible light D^(2) NN classifier is used to recognize unchanged targets(handwritten digits ranging from 0 to 9)and targets that have been changed(i.e.,targets that have been covered or altered)at a visible wavelength of 632.8 nm.The obtained experimental classification accuracy(84%)and numerical classification accuracy(91.57%)quantify the match between the theoretical design and fabricated system performance.The presented framework can be used to apply a D^(2) NN to various practical applications and design other new applications.

Six-State Quantum Key Distribution Using Photons with Orbital Angular Momentum

A new implementation of high-dimensional quantum key distribution （QKD） protocol is discussed. Using three mutual unbiased bases, we present a d？level six-state QKD protocol that exploits the orbital angular momentum with the spatial mode of the light beam. The protocol shows that the feature of a high capacity since keys are encoded using photon modes in d-level Hilbert space. The devices for state preparation and measurement are also discussed. This protocol has high security and the alignment of shared reference frames is not needed between sender and receiver.

Single-shot mid-infrared incoherent holography using Lucy-Richardson-Rosen algorithm

In recent years,there has been a significant transformation in the field of incoherent imaging with new possibilities of compressing three-dimensional(3D)information into a two-dimensional intensity distribution without two-beam interference(TBI).Most of the incoherent 3D imagers without TBI are based on scattering by a random phase mask exhibiting sharp autocorrelation and low cross-correlation along the depth.Consequently,during reconstruction,high lateral and axial resolutions are obtained.Imaging based on scattering requires an astronomical photon budget and is therefore precluded in many power-sensitive applications.In this study,a proof-of-concept 3D imaging method without TBI using deterministic fields has been demonstrated.A new reconstruction method called the Lucy-Richardson-Rosen algorithm has been developed for this imaging concept.We believe that the proposed approach will cause a paradigm-shift in the current state-of-the-art incoherent imaging,fluorescence microscopy,mid-infrared fingerprinting,astronomical imaging,and fast object recognition applications.

Photonic spin Hall effect:fundamentals and emergent applications

The photonic spin Hall effect(SHE)refers to the transverse spin separation of photons with opposite spin angular momentum,after the beam passes through an optical interface or inhomogeneous medium,manifested as the spin-dependent splitting.It can be considered as an analogue of the SHE in electronic systems:the light’s right-circularly polarized and left-circularly polarized components play the role of the spin-up and spin-down electrons,and the refractive index gradient replaces the electronic potential gradient.Remarkably,the photonic SHE originates from the spin-orbit interaction of the photons and is mainly attributed to two different geometric phases,i.e.,the spin-redirection Rytov-Vlasimirskii-Berry in momentum space and the Pancharatnam-Berry phase in Stokes parameter space.The unique properties of the photonic SHE and its powerful ability to manipulate the photon spin,gradually,make it a useful tool in precision metrology,analog optical computing and quantum imaging,etc.In this review,we provide a brief framework to describe the fundamentals and advances of photonic SHE,and give an overview on the emergent applications of this phenomenon in different scenes.

Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network

Large-scale linear operations are the cornerstone for performing complex computational tasks.Using optical computing to perform linear transformations offers potential advantages in terms of speed,parallelism,and scalability.Previously,the design of successive spatially engineered diffractive surfaces forming an optical network was demonstrated to perform statistical inference and compute an arbitrary complex-valued linear transformation using narrowband illumination.We report deep-learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily selected,complex-valued linear transformations between an input and output field of view,each with Ni and No pixels,respectively.This broadband diffractive processor is composed of Nw wavelength channels,each of which is uniquely assigned to a distinct target transformation;a large set of arbitrarily selected linear transformations can be individually performed through the same diffractive network at different illumination wavelengths,either simultaneously or sequentially(wavelength scanning).We demonstrate that such a broadband diffractive network,regardless of its material dispersion,can successfully approximate Nw unique complex-valued linear transforms with a negligible error when the number of diffractive neurons(N)in its design is≥2NwNiNo.We further report that the spectral multiplexing capability can be increased by increasing N;our numerical analyses confirm these conclusions for Nw>180 and indicate that it can further increase to Nw∼2000,depending on the upper bound of the approximation error.Massively parallel,wavelength-multiplexed diffractive networks will be useful for designing highthroughput intelligent machine-vision systems and hyperspectral processors that can perform statistical inference and analyze objects/scenes with unique spectral properties.

Preparation of Arbitrary Four-Qubit W State with Atomic Ensembles via Rydberg Blockade

The generation of various entangled states is an essential task in quantum information processing. Recently, a scheme （PRA 79, 022304） has been suggested for generating Greenberger-Horne-Zeilinger state and cluster state with atomic ensembles based on the Rydberg blockade. Using similar resources as the earlier scheme, here we propose an experimentally feasible scheme of preparing arbitrary four-qubit W class of maximally and non- maximally entangled states with atomic ensembles in a single step. Moreover, we carefully analyze the realistic noises and predict that four-qubit W states can be produced with high fidelity （F - 0.994） via our scheme.

Efficient Phase Locking of Fiber Amplifiers Using a Low-Cost and High-Damage-Threshold Phase Control System

We propose a low-cost and high-damage-threshold phase control system that employs a piezoelectric ceramic transducer modulator controlled by a stochastic parallel gradient descent algorithm. Efficient phase locking of two fiber amplifiers is demonstrated. Experimental results show that energy encircled in the target pinhole is increased by a factor of 1.76 and the visibility of the fringe pattern is as high as 90% when the system is in close-loop. The phase control system has potential in phase locking of large-number and high-power fiber laser endeavors.

Preparation of Cluster States of Atomic Qubits in Cavity QED

We propose an optical scheme to generate cluster states of atomic qubits, with each trapped in separate optical cavity, via atom-cavity-laser interaction. The quantum information of each qubit is encoded on the degenerate ground states of the atom, hence the entanglement between them is relatively stable against spontaneous emission. A single-photon source and two classical fields are employed in the present scheme. By controlling the sequence and time of atom-cavity-laser interaction, we show that the atomic cluster states can be produced deterministically.

Implementing a Multi-Qubit Quantum Phase Gate Encoded by Photonic Qubit

A protocol is proposed to implement a three-qubit phase gate for photonic qubits in a three-mode cavity. The idea can be extended to directly implement a N-qubit phase gate. We also show that the interaction time remains unchanged with the increasing number of qubits. In addition, the influence of cavity decay and atomic spontaneous emission on the gate fidelity and photon loss probability is also discussed by numerical calculation.