All-optical Networks Research Articles

All-optical Einstein-Podolsky-Rosen steering swapping.

Einstein-Podolsky-Rosen (EPR) steering, an important resource in quantum information, describes the ability of one party to influence the state of another party through local measurements. It differs from Bell nonlocality and entanglement due to its asymmetric property. EPR steering swapping allows two spatially independent parties to present EPR steering without direct interaction. Here, we theoretically propose an all-optical EPR steering swapping (AOSS) scheme based on low-noise high-broadband optical parametric amplifiers (OPAs). A low-noise high-broadband OPA is utilized to implement the function of Bell-state measurement without detection, avoiding the optic-electro and electro-optic conversions. After AOSS, one-way and two-way EPR steering between two independent optical modes can be obtained. Our scheme provides a guidance for the construction of a measurement-free, all-optical broadband quantum network utilizing EPR steering swapping.

Multiplexable all-optical nonlinear activator for optical computing

As an alternative solution to surpass electronic neural networks, optical neural networks (ONNs) offer significant advantages in terms of energy consumption and computing speed. Despite the optical hardware platform could provide an efficient approach to realizing neural network algorithms than traditional hardware, the lack of optical nonlinearity limits the development of ONNs. Here, we proposed and experimentally demonstrated an all-optical nonlinear activator based on the stimulated Brillouin scattering (SBS). Utilizing the exceptional carrier dynamics of SBS, our activator supports two types of nonlinear functions, saturable absorption and rectified linear unit (Relu) models. Moreover, the proposed activator exhibits large dynamic response bandwidth (∼11.24 GHz), low nonlinear threshold (∼2.29 mW), high stability, and wavelength division multiplexing identities. These features have potential advantages for the physical realization of optical nonlinearities. As a proof of concept, we verify the performance of the proposed activator as an ONN nonlinear mapping unit via numerical simulations. Simulation shows that our approach achieves comparable performance to the activation functions commonly used in computers. The proposed approach provides support for the realization of all-optical neural networks.

Dynamically reconfigurable all-optical neural network based on a hybrid graphene metasurface array

In recent years, optical neural networks (ONNs) have received considerable attention for their intrinsic parallelism and low energy consumption, making them a vital area of research. However, the current passive diffractive ONNs lack dynamic tunability after fabrication for specific tasks. Here, we propose a dynamically reconfigurable diffractive deep neural network based on a hybrid graphene metasurface array, wherein the transmission and refractive index of each pixel can be finely adjusted via gate voltage. This capability enables the tailored modulation of the incident light’s amplitude and phase at each pixel, aligning with specific task requirements. The simulation results show the attainability of a dynamic modulation range of 7.97dB (ranging from −8.56dB to −0.591dB). Additionally, this proposed diffractive neural network platform incorporates an ultrathin structure comprising a one-atom-thick graphene layer and nanoscale metallic metastructures, rendering it compatible with complementary metal-oxide-semiconductor technology. Notably, a classification accuracy of 92.14% for a single-layer neural network operating in the terahertz spectrum is achieved based on the calculation result. This proposed platform presents compelling prospects for constructing various artificial neural network architectures with applications ranging from drug screening to automotive driving and vision sensing.

Deterministic All-Optical Continuous-Variable Quantum Telecloning.

Quantum telecloning, a pivotal multiuser quantum communication protocol in the realm of quantum information science, facilitates the copy of a quantum state across M distinct locations through teleportation technique. In the continuous-variable regime, the implementation of quantum telecloning necessitates the distribution of multipartite entanglement among the sender and M receiver parties. Following this, the sender carries out optic-electro conversion and transmits information via classical channel to M spatially separated receivers simultaneously. To successfully reconstruct the input state, electro-optic conversion needs to be employed by each receiver. However, due to these conversions, the bandwidth of the optical mode in this process is largely constrained. In this Letter, we present an all-optical version of the 1→2 continuous-variable quantum telecloning scheme, wherein both optic-electro and electro-optic conversions are replaced by optical components. Our scheme allows the two receivers to achieve input state reconstruction solely by utilizing beam splitters, significantly simplifying its complexity. We experimentally demonstrate all-optical 1→2 quantum telecloning of coherent state and achieve the fidelities of 58.6%±1.0% and 58.6%±1.1% for two clones, exceeding the corresponding classical limits (51.9%±0.5% and 51.9%±0.6%). Our results establish a platform for constructing a flexible all-optical multiuser quantum network and promote the field of all-optical quantum information processing.

Three-Soliton Interactions and the Implementation of Their All-Optical Switching Function

As a key component in all-optical networks, all-optical switches play a role in constructing all-optical switching. Due to the absence of photoelectric conversion, all-optical networks can overcome the constraints of electronic bottlenecks, thereby improving communication speed and expanding their communication bandwidth. We study all-optical switches based on the interactions among three optical solitons. By analytically solving the coupled nonlinear Schrödinger equation, we obtain the three-soliton solution to the equation. We discuss the nonlinear dynamic characteristics of various optical solitons under different initial conditions. Meanwhile, we analyze the influence of relevant physical parameters on the realization of all-optical switching function during the process of three-soliton interactions. The relevant conclusions will be beneficial for expanding network bandwidth and reducing power consumption to meet the growing demand for bandwidth and traffic.

Spatial Writing of Ultrafast All-Optical Switching.

Writing spatial information on ultrafast all-optical switching is essential for constructing ultrafast processing units in photonic applications, such as optical communication and computing networks. However, most methods ignore the fabrication and imaging of controllable switching area, limiting its spatial information and the further design in ultrafast devices. Here, we propose a method to spatially write in ultrafast all-optical switching based on MAPbI3 perovskite with nanocone structure and visualize the switching effect in arbitrary designed area. Due to the light confinement effect of nanocone fabrication using a fs laser, the light is strongly absorbed by perovskite and reach saturable absorption. It leads to ultrafast broadband transmittance change with 25 fs switching time and 10% modulation depth in nanocone perovskite area. Our preparation method offers high efficiency, performance, and flexibility for the spatial writing of ultrafast all-optical switching, which is promising for developing ultrafast all-optical networks and the next generation of communication technology.

All-Optical Diffractive Deep Neural Networks Enabled Laser-Reduced Graphene Oxide Tactile Sensor for Braille Recognition

All-Optical Diffractive Deep Neural Networks Enabled Laser-Reduced Graphene Oxide Tactile Sensor for Braille Recognition

Fast and scalable all-optical network architecture for distributed deep learning

With the ever-increasing size of training models and datasets, network communication has emerged as a major bottleneck in distributed deep learning training. To address this challenge, we propose an optical distributed deep learning (ODDL) architecture. ODDL utilizes a fast yet scalable all-optical network architecture to accelerate distributed training. One of the key features of the architecture is its flow-based transmit scheduling with fast reconfiguration. This allows ODDL to allocate dedicated optical paths for each traffic stream dynamically, resulting in low network latency and high network utilization. Additionally, ODDL provides physically isolated and tailored network resources for training tasks by reconfiguring the optical switch using LCoS-WSS technology. The ODDL topology also uses tunable transceivers to adapt to time-varying traffic patterns. To achieve accurate and fine-grained scheduling of optical circuits, we propose an efficient distributed control scheme that incurs minimal delay overhead. Our evaluation on real-world traces showcases ODDL’s remarkable performance. When implemented with 1024 nodes and 100 Gbps bandwidth, ODDL accelerates VGG19 training by 1.6× and 1.7× compared to conventional fat-tree electrical networks and photonic SiP-Ring architectures, respectively. We further build a four-node testbed, and our experiments show that ODDL can achieve comparable training time compared to that of an ideal electrical switching network.

Optical authentication scheme based on all-optical neural network.

Diffractive deep neural network is architectural designs based on the principles of neural networks, which consists of multiple diffraction layers and has the remarkable ability to perform machine learning tasks at the speed of light. In this paper, a novel optical authentication system was presented that utilizes the diffractive deep neural network principle. By carefully manipulating a light beam with both a public key and a private key, we are able to generate a unique and secure image representation at a precise distance. The generated image can undergo authentication by being processed through the proposed authentication system. Leveraging the utilization of invisible terahertz light, the certification system possesses inherent characteristics of concealment and enhanced security. Additionally, the entire certification process operates solely through the manipulation of the light beam, eliminating the need for electronic calculations. As a result, the system offers rapid certification speed. The proposed optical authentication scheme is further validated through computer simulations, which showcase its robust security and high precision. This method holds immense potential for diverse applications in optical neural network authentication, warranting a broad scope of future prospects.

Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

Machine learning with optical neural networks has featured unique advantages of the information processing including high speed, ultrawide bandwidths and low energy consumption because the optical dimensions (time, space, wavelength, and polarization) could be utilized to increase the degree of freedom. However, due to the lack of the capability to extract the information features in the orbital angular momentum (OAM) domain, the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network model. Here, we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network (CNN) based on Laguerre-Gaussian (LG) beam modes with diverse diffraction losses. The proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction, and deep-learning diffractive layers as a classifier. The resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding, leading to an accuracy as high as 97.2% for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes, as well as a resistance to eavesdropping in point-to-point free-space transmission. Moreover, through extending the target encoded modes into multiplexed OAM states, we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%. Our work provides a deep insight to the mechanism of machine learning with spatial modes basis, which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

All-optical neural network nonlinear activation function based on the optical bistability within a micro-ring resonator

Training all-optical neural networks in itself remains an unresolved problem, and the challenges compound when the problem is turned into the hardware implementations. In this paper, we propose a nonlinear activation function based on optical bistability within a micro-ring resonator (MRR), achieving threshold control without external modulation. Furthermore, a convolutional neural network similar to the Le-Net-5 architecture is designed, in which all nonlinear activation functions are composed of optical bistable hysteresis loop. The numerical simulation results demonstrate that the recognition rate on the Fashion-MNIST dataset can achieve 91.3%, which means that the optical neuromorphic computation can be implemented by utilizing the nonlinear optical effects themselves in the all-optical hardware. Such a scheme promises access to the all-optical neural network training in the optical hardware environment compared to numerical activation functions.

Dual-Band All-Optical Logic Gates by Coherent Absorption in an Amorphous Silicon Graphene Metasurface.

The dual-band polarization-independent all-optical logic gate by coherent absorption effect in an amorphous silicon (a-Si) graphene metasurface is investigated theoretically and numerically. Taking the substrate effect into consideration, the coherent perfect absorption condition of the a-Si graphene metasurface is derived on the basis of the Cartesian multipole method. The coherent nearly perfect absorption of the a-Si graphene metasurface is realized by the interference of multipole moments and the interband transition of monolayer graphene, achieving peak values of 91% and 92% at 894.5 nm and 991.5 nm, respectively. The polarization independence of the coherent absorption is revealed due to the center symmetry of the structure of the a-Si graphene metasurface. The dual-band polarization-independent all-optical XOR and OR logic gates are implemented at 894.5 nm and 991.5 nm by the a-Si graphene metasurface based on the coherent nearly perfect absorption, which has the opportunity to be utilized in all-optical computing, all-optical data processing, and future all-optical networks.

Role of all-optical neural networks

Role of all-optical neural networks

All-optical logic gates based on topological edge and corner states in two-dimensional photonic crystals with square dielectric columns

Recently, with the rapid progress in all-optical networks and optical computing, there is an increasing requirement for more appropriate methods to design all-optical logic gates. Photonic crystals (PCs) can be serving as a versatile platform for manipulating light propagation. The realization of topological edge states (TESs) and topological corner states (TCSs) within high-order topological photonic insulators has attracted extensive attention. In this paper, TESs and TCSs are achieved using honeycomb PCs with square dielectric columns instead of conventional cylindrical ones for obtaining a larger photonic energy band gap due to reduction of dielectric column symmetry. TESs with overlapping frequencies can be attained by different arrangements of combining two PCs with distinct topological properties. A sandwich structure comprising both topologically trivial and non-trivial PCs is proposed, and ‘AND Gate’ and ‘OR Gate’ logic gates are implemented through the coupling between edge state waveguides when controlling the number of coupling layers. Additionally, a triangular-shaped box structure composed of non-trivial PCs enveloped by trivial PCs is constructed. Within this structure, TCSs manifest only around each acute angle, and a ‘NOT Gate’ logic gate is realized through corner state coupling and edge state coupling. This work paves a new way of designing high-performance micro–nano all-optical logic gate devices.

Fast QoT estimation method using cascaded artificial neural network for real-time path provisioning in IMDD based all-optical networks.

We propose a fast quality of transmission (QoT) estimation method based on cascaded artificial neural networks (ANNs) for intensity modulation-direct detection (IMDD) systems. The proposed method can calculate the bit error rate of three-span 36 km transmission within 0.7 seconds while taking account of the deterministic waveform distortion caused by the chromatic dispersion and self-phase modulation (SPM).

Joint resource optimization and flexible QoS provision using hybrid optical core node architecture

All-optical networks (AON) provide large bandwidth, high-speed switching, and quality of service (QoS). However, the emergence of recent applications generating flexible traffic demands affects AON resource utilization and QoS management. These concerns are handled by AON using core nodes with large wavelength converters (WC) and fiber delay line (FDL) spaces, resulting in a high cost of implementation and operation. This paper proposes a hybrid core node architecture, upgrading existing nodes with limited effective modification to minimize physical topology costs. This hybrid architecture increases WC and FDL availability and reduces wavelength utilization according to traffic behavior. These new node functions are enabled using an advanced algorithm integrating a novel cost formulation merging WC and FDL with lightpath deflection (LD). This algorithm activates resource availabilities using a flexible QoS scheme considering the required and real-time traffic loss and blocking delay. The experimental results indicate that the proposed approach optimizes AON resources while guaranteeing required traffic demands compared to the existing techniques.

Piston sensing for sparse aperture systems via all-optical diffractive neural network

Piston sensing for sparse aperture systems via all-optical diffractive neural network

Simulating an Integrated Photonic Image Classifier for Diffractive Neural Networks.

The slowdown of Moore's law and the existence of the "von Neumann bottleneck" has led to electronic-based computing systems under von Neumann's architecture being unable to meet the fast-growing demand for artificial intelligence computing. However, all-optical diffractive neural networks provide a possible solution to this challenge. They can outperform conventional silicon-based electronic neural networks due to the significantly higher speed of the propagation of optical signals (≈108 m.s-1) compared to electrical signals (≈105 m.s-1), their parallelism in nature, and their low power consumption. The integrated diffractive deep neural network (ID2NN) uses an on-chip fully passive photonic approach to achieve the functionality of neural networks (matrix-vector operations) and can be fabricated via the CMOS process, which is technologically more amenable to implementing an artificial intelligence processor. In this paper, we present a detailed design framework for the integrated diffractive deep neural network and corresponding silicon-on-insulator integration implementation through Python-based simulations. The performance of our proposed ID2NN was evaluated by solving image classification problems using the MNIST dataset.

Metasurface-Based Optical Logic Operators Driven by Diffractive Neural Networks.

In this paper, a novel optical logic operator based on the multifunctional metasurface driven by all-optical diffractive neural network is reported, which can perform four principal quantum logic operations (Pauli-X, Pauli-Y, Pauli-Z, and Hadamard gates). The two ground states and are characterized by two orthogonal linear polarization states. The proposed spatial- and polarization-multiplexed all-optical diffractive neural network only contains a hidden layer physically mapped as a metasurface with simple and compact unit cells, which dramatically reduces the volume and computing resources required for the system. The designed optical quantum operator is proven to achieve high fidelities for all four quantum logical gates, up to 99.96% numerically and 99.88% experimentally. The solution will facilitate the construction of large-scale optical quantum computing systems and scalable optical quantum devices.

All-Optical Switches for Optical Soliton Interactions in a Birefringent Fiber

Interactions among optical solitons can be used to develop photonic information processing devices such as all-optical switches and all-optical logic gates. It is the key to achieve high-speed, high-capacity all-optical networks and optical computers, which is also important in academy. We study the properties of all-optical switches of optical solitons in birefringent fibers, based on the coupled nonlinear Schrödinger equations. It is found that under different initial conditions we can achieve all-optical switching functions. We also study the influence of different physical parameters of birefringent fibers on all-optical soliton switching. The relevant conclusions are conducive to achieving the all-optical switching function of optical solitons in birefringent fibers, providing useful guidance for widespread applications of optical soliton all-optical switches in birefringent fibers of communications.