Abstract Optical computing chips have emerged as a transformative computing technology due to their high computational density, low energy consumption, and compact footprint. While real- and complex-valued computing chips have been well developed, their fundamental limitations in representing high-dimensional data significantly constrain their applicability in modern signal processing. Quaternions enable direct operations on three- and four-dimensional data, powering high-dimensional processing in data analytics and artificial intelligence. Here we demonstrate a quaternion optical computing chip (QOCC) for the first time and benchmark its performance in several typical application scenarios: three-dimensional point cloud processing, RGB chromatic transformation, and quaternion convolutional neural network for color image recognition. The QOCC harnesses high parallelism of light by wavelength-division multiplexing, processing high-dimensional data simultaneously through multiple optical wavelength channels. Unlike conventional real-valued photonic accelerators, we propose a quaternion-dedicated hardware topology that physically maps the non-Abelian Hamilton product onto a multi-dimensional multiplexing photonic circuit. Compared to the electronic computing counterpart, our QOCC achieves higher computational fidelity (root mean square error < 0.035) and substantially reduced computational load (2/3 lower). It paves the way towards next-generation optical computing, overcoming the limitations of traditional computing systems in high-dimensional data processing.
Abstract Optical computing chips have emerged as a transformative computing technology due to their high computational density, low energy consumption, and compact footprint. While real- and complex-valued computing chips have been well developed, their fundamental limitations in representing high-dimensional data significantly constrain their applicability in modern signal processing. Quaternions enable direct operations on three- and four-dimensional data, powering high-dimensional processing in data analytics and artificial intelligence. Here we demonstrate a quaternion optical computing chip (QOCC) for the first time and benchmark its performance in several typical application scenarios: three-dimensional point cloud processing, RGB chromatic transformation, and quaternion convolutional neural network for color image recognition. The QOCC harnesses high parallelism of light by wavelength-division multiplexing, processing high-dimensional data simultaneously through multiple optical wavelength channels. Unlike conventional real-valued photonic accelerators, we propose a quaternion-dedicated hardware topology that physically maps the non-Abelian Hamilton product onto a multi-dimensional multiplexing photonic circuit. Compared to the electronic computing counterpart, our QOCC achieves higher computational fidelity (root mean square error < 0.035) and substantially reduced computational load (2/3 lower). It paves the way towards next-generation optical computing, overcoming the limitations of traditional computing systems in high-dimensional data processing.
In recent years, quantum information has rapidly evolved as a field with transformative potential in science and technology. It provides secure encryption for communication, accurate emulation of practical physical systems, streamlined and accelerated machine learning models, and advanced computational powers that outperform classical supercomputers in solving complex problems. These advancements are all reliant on the superposition and entanglement properties of qubits. While various candidates such as trapped ions or superconducting qubits have been proposed to realize quantum technologies, photons have demonstrated a prominent advantage due to their robustness against decoherence and noise, high clock cycle rate, fast and easy measurement, and room temperature working environment. In addition, photonic integrated chip (PIC) technology has developed rapidly since the beginning of the 21st century. The number of devices integrated on a single chip has increased rapidly, and its complexity has also increased. Quantum computing combined with integrated photonics is expected to solve bottleneck problems in practical situations. In this thesis, based on the silicon-on-insulator (SOI) platform, an optical computing chip and a large-scale quantum computing chip with various practical applications are specifically studied. In the first part of the thesis, a novel integrated diffractive optical network is studied, and a scheme is designed to realize parallel Fourier transform, convolution operations, and application-specific optical computing. Due to the utilization of on-chip compact diffractive cells (slab waveguides), the footprint and power consumption of the proposed architecture is reduced from quadratic scaling in the input data dimensions required for Mach-Zehnder interferometer (MZI)-based optical neural network (ONN) architectures to linear scaling for the improved deep neural networks. A 10-fold reduction in both footprint and energy consumption and equal high accuracy with previous MZI-based ONNs was experimentally achieved for computations performed on the Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets. This reduction in the resource scaling from quadratic to linear has a profound impact on the realization of large-scale silicon-photonics computing circuits with current fabrication technologies. A quantum photonic microprocessor based on Gaussian Boson sampling (GBS) that offers dynamic programmability to solve various graph-related NP-complete problems is then demonstrated. The system with optical, electrical, and thermal packaging implements a GBS with 16 modes of single-mode squeezed vacuum states, a universal programmable 16-mode interferometer, and a single photon readout on all outputs with high accuracy, generality, and controllability. The developed system is applied to demonstrate applications in solving NP-complete problems, manifesting the ability of photonic quantum computing to realize practical applications for conventionally intractable computations. The GBS-based quantum photonic microprocessor is applied to solve task assignment, Boolean satisfiability, graph clique, max cut, and vertex cover. These demonstrations suggest an excellent benchmarking platform, paving the way toward large-scale combinatorial optimization. Finally, this thesis explores the application possibility of large-scale PICs in molecular simulation. A new algorithm utilizing states with zero displacements (vacuum-squeezed states) coupling to a linear network is proposed to calculate molecular vibronic spectra. With a large-scale PIC, molecular vibronic spectra are accurately simulated for formic acid and thymine with high reconstructed fidelity (>91%) and for naphthalene, phenanthrene, and benzene under the non-Condon regime. Our experimental demonstration will open pathways toward conducting large-scale molecular quantum simulations, a task surpassing classical computers’ capabilities.
Integrated photonic chips leverage the recent developments in integrated circuit technology, along with the control and manipulation of light signals, to realize the integration of multiple optical components onto a single chip. By exploiting the power of light, integrated photonic chips offer numerous advantages over traditional optical and electronic systems, including miniaturization, high-speed data processing and improved energy efficiency. In this review, we survey the current status of quantum computation, optical neural networks and the realization of some algorithms on integrated optical chips.
Ising problems are critical for a wide range of applications. Solving these problems on a photonic platform takes advantage of the unique properties of photons, such as high speed, low power consumption, and large bandwidth. Recently, there has been growing interest in using photonic platforms to accelerate the optimization of Ising models, paving the way for the development of ultrafast hardware in machine learning. However, these proposed systems face challenges in simultaneously achieving high spin scalability, encoding flexibility, and low system complexity. We propose a wavelength-domain optical Ising machine that utilizes optical signals at different wavelengths to represent distinct Ising spins for Ising simulation. We design and experimentally validate a chip-scale Ising machine capable of solving classical non-deterministic polynomial-time problems. The proposed Ising machine supports 32 spins and features 2 distinct coupling encoding schemes. Furthermore, we demonstrate the feasibility of scaling the system to 256 spins. This approach verifies the viability of performing Ising simulations in the wavelength dimension, offering substantial advantages in scalability. These advancements lay the groundwork for future large-scale expansion and practical applications in cloud computing.
In the context of rapid technological advancements, progress in chip manufacturing technology is propelling electronic devices toward miniaturization and high efficiency at an unprecedented pace. The fundamental principles of quantum mechanics are beginning to play a crucial role in modern electronic devices, providing the foundation for new technological methods and research directions. Optical quantum signal processing, as an important branch of quantum technology, utilizes the quantum properties of photons for information processing and transmission, offering advantages such as high bandwidth, low noise, and strong resistance to interference. However, current optical quantum measurement and control instruments face significant challenges in terms of real-time performance and programming complexity, resulting in reduced data processing efficiency and significantly increased development costs. This study implements a real-time processing algorithm for optical quantum signals on a Field-Programmable Gate Array (FPGA). The algorithm possesses advantages of real-time performance, low power consumption, and high integration. It allows for rapid functionality switching and efficient optical quantum signal processing. The design specifically aims to enhance the signal-to-noise ratio, significantly improving measurement accuracy and reliability. Experimental results indicate that this method substantially enhances the measurement precision and signal-to-noise ratio of optical quantum signals. This research provides an efficient and reliable new method for optical quantum signal processing, laying a solid foundation for further studies in quantum communication, quantum computing, and quantum precision measurement.
In recent years, optical computing becomes a powerful task-specific platform, under the increasing demand for computing power (especially for neural networks). This doctoral thesis is devoted to the design, simulation, and testing of integrated silicon photonic circuits for optical neural networks and machine-learning-assisted quantum computing tasks. In Chapter 3, we demonstrate an optical neural chip that implements complex-valued neural networks and benchmark its performance under practical settings, proving strong learning capabilities. In Chapter 4, we demonstrate an efficient, physics-agnostic, and closed-loop protocol for training optical neural networks on chip. In Chapter 5, we demonstrate a silicon photonic chip that achieves chip-to-chip teleportation of high-dimensional quantum states, using the trainable quantum autoencoders. High fidelities are achieved between the input qutrit and the qutrit decoded from the teleported state. Our results present a promising avenue towards realizing deep optical neural networks and challenging quantum computing tasks with dedicated integrated silicon photonic circuits.
Artificial intelligence-driven (AI-driven) data centers, which require high-performance, scalable, energy-efficient, and secure infrastructure, have led to unprecedented data traffic demands. These demands involve low-latency, high-bandwidth connections, low power consumption, and data confidentiality. However, conventional optical interconnect solutions, such as intensity-modulated direct detection and traditional coherent systems, cannot address these requirements simultaneously. In particular, conventional encryption protocols that rely on complex algorithms are increasingly vulnerable to the rapid advancement of quantum computing. We propose and demonstrate a quantum-secured data transmission architecture that involves minimal digital signal processing (DSP) consumption and meets all the stringent requirements for AI-driven data center optical interconnect (AI-DCI) scenarios. By integrating a self-homodyne coherent (SHC) system and quantum key distribution (QKD) through the multicore-fiber-based space division multiplexing (SDM) technology, our scheme enables secure, high-capacity, and energy-efficient data transmission while ensuring resilience against quantum computing threats. In our demonstration, we achieved an expandable transmission capacity of 2 Tbit per second (Tb/s) with a quantum secret key rate (SKR) of 229.2 kb/s and further validated real-time encrypted transmission using AES-256 encryption with QKD-generated keys. Our work paves the way for constructing secure, scalable, and cost-efficient data transmission frameworks, enabling the next generation of intelligent, leak-proof optical interconnects for data centers.