Quantum information has been emerging as a fast-developing subject in recent years. It has shown the potential in bringing revolution to science and technology progress such as highly accurate and efficient simulation of practical physical systems, more compact and faster machine learning models for prediction and advanced quantum computational performance to solve intractable problems for classical supercomputers. Among many candidates to realize quantum technologies, the photon has demonstrated its prominent advantage for its robustness and ease of measurement. This thesis mainly focuses on the photonic integrated quantum technologies to produce and manipulate photons on an integrated silicon chip and characterizes its performance along with the demonstration of three quantum computing: GBS quantum computing for the perfect matching problem, quantum error correction and quantum teleportation with autoencoder. The theoretical modeling, photonic integrated circuit design and experimental implementation process are described detailly in this thesis.
Quantum information has been emerging as a fast-developing subject in recent years. It has shown the potential in bringing revolution to science and technology progress such as highly accurate and efficient simulation of practical physical systems, more compact and faster machine learning models for prediction and advanced quantum computational performance to solve intractable problems for classical supercomputers. Among many candidates to realize quantum technologies, the photon has demonstrated its prominent advantage for its robustness and ease of measurement. This thesis mainly focuses on the photonic integrated quantum technologies to produce and manipulate photons on an integrated silicon chip and characterizes its performance along with the demonstration of three quantum computing: GBS quantum computing for the perfect matching problem, quantum error correction and quantum teleportation with autoencoder. The theoretical modeling, photonic integrated circuit design and experimental implementation process are described detailly in this thesis.
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.
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.
Recent advancements in quantum photonics have driven significant progress in photonic quantum computing (PQC), addressing challenges in scalability, efficiency, and fault tolerance. Experimental efforts have focused on integrated photonic platforms utilizing materials such as silicon photonics and lithium niobate to enhance performance. Parameters like photon loss rates, coupling efficiencies, and fidelities have been pivotal, with state-of-the-art systems achieving coupling efficiencies above 90% and photon indistinguishability exceeding 99%. Quantum error correction schemes have reduced logical error rates to below 10−3, marking a step toward fault-tolerant PQC. Photon generation has also advanced with deterministic sources, such as quantum dots, achieving brightness levels exceeding 106 photon pairs/s/mW and time-bin encoding enabling scalable entanglement. Heralded single-photon sources now exhibit purities above 99%, driven by innovations in fabrication techniques. High-efficiency photon detectors, such as superconducting nanowire single-photon detectors (SNSPDs), have demonstrated detection efficiencies exceeding 98%, dark count rates below 1 Hz, and timing jitters as low as 15 ps, ensuring precise photon counting and manipulation. Moreover, demonstrations of boson sampling with over 100 photons underscore the growing computational power of photonic systems, surpassing classical limits. The integration of machine learning has optimized photonic circuit design, while frequency multiplexing and time-bin encoding have increased system scalability. Together, these advances bridge the gap between theoretical potential and practical implementation, positioning PQC as a transformative technology for computing, communication, and quantum sensing.
Optical chips for quantum photonics are cutting-edge technology, merging photonics and quantum mechanics to manipulate light at the quantum level. These chips are crucial for advancing quantum computing, secure communication, and precision sensing by integrating photonic components such as waveguides, beam splitters, and detectors to manipulate single photons, the fundamental carriers of quantum information. Key advancements in optical chips include low-loss waveguides, efficient single-photon sources, and high-fidelity quantum gates, all essential for scalable quantum circuits. Integrating these circuits on a chip offers significant advantages in miniaturization, stability, and reproducibility over traditional bulk optics setups. Recent breakthroughs in materials science and nanofabrication have propelled the field forward, enabling the production of chips with higher precision and lower defect rates. Silicon photonics, in particular, has become a prominent platform due to its compatibility with existing semiconductor manufacturing processes, facilitating the integration of quantum photonic circuits with classical electronic systems. Here, we share our vision of the future of optical chips for quantum photonics, which hold promise for various applications. In quantum computing, they enable the development of compact and scalable quantum processors. In communication, they provide the foundation for ultra-secure quantum networks through quantum key distribution. In sensing, they allow for high-precision measurements that surpass classical limits. As research progresses, optical chips are expected to play a critical role in realizing the full potential of quantum technologies.
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.
Quantum technologies harness the properties of controlled quantum mechanical systems for applications in computation, communication and metrology, and enable us to further our understanding of fundamental physics. Quantum hardware is designed to manipulate complex and fragile many-particle quantum states, which requires exquisite control machinery, advanced software and near-complete isolation from the surrounding environment. With sufficient capabilities, quantum computers with large registers of quantum bits will perform classically intractable calculations such as quantum chemistry simulations. Quantum key distribution will provide completely secure communications across the world and quantum metrology will enable measurement precision beyond today's capabilities. While quantum technologies are far from surpassing classical hardware today, the vision and potential impact has sparked a world-wide research effort, both theoretical and experimental, to develop a quantum computer. The most sophisticated current quantum technologies have control over small numbers of quantum bits and are limited by environmental decoherence processes. While progress in developing quantum hardware is ongoing, designing and demonstrating new algorithms and protocols for quantum information is a thriving research field. Photonics provides an ideal platform for small-scale proof-of-concept quantum experiments, which are the focus of this thesis. In the coming decades, we will see the realisation of quantum hardware capable of applications that outperform any classical computer. In this thesis, I present several protocols and algorithms for quantum information science and technology, which are implemented in quantum photonic experiments. The applications of these works include robust quantum tomography, quantum state relocation, quantum enhanced data recovery and probing fundamental causality in quantum mechanics. The works presented here are based on two photon experiments from a prototypical spontaneous parametric down-conversion source, which provides an excellent test-bed for quantum information experiments. Photons can be used to encode quantum information in a range of different degrees of freedom. This versatility, along with compatibility with existing technology gives photonics a great advantage in the development of quantum hardware.