Quantum computing, based on the inherent superposition and entanglement properties of quantum states, can break through the limits of classical computing power. However, under the present technical conditions, the number of qubits that can be manipulated is still limited. In addition, the preparation of high-precision quantum gates and additional quantum error correction systems requires more auxiliary bits, which leads to extra cost. Therefore, it seems to be a long-term goal to realize a universal fault-tolerant quantum computer. The development of analog quantum computing is a transition path that can be used to simulate many-body physics problems. Quantum walk, as the quantum counterpart of classical random walks, is a research hotspot in analog quantum computing. Owing to the unique quantum superposition characteristics, quantum walk exhibits the ballistic transport properties of outward diffusion, so quantum walk provides acceleration in computing power for various algorithms. Based on quantum walk, different computing models are derived to deal with practical physical problems in different fields, such as biology, physics, economics, and computer science. A large number of technical routes are devoted to the experiments on realizing quantum walk, including optical fiber networks, superconducting systems, nuclear magnetic resonance systems, and trapped ion atom systems. Among these routes, photons are considered as the reliable information carriers in the experiments on quantum walking due to their controllability, long coherence time. and fast speed. Therefore, in this review, we focus on different quantum walk theories and experimental implementations in optical versions, such as traditional optical platforms, optical fiber platforms, and integrated optical quantum platform. In recent years, the rapid development of integrated optical quantum platforms has driven the experiments on quantum walk to move towards the stage of integration and miniaturization, and at the same time, the experimental scale and the number of qubits have gradually increased. To this end, we summarize the technological progress of integrated optical quantum computing, including various integrated optical quantum experimental platforms and their applications. Secondly, we specifically discuss the experiment on quantum walk and practical applications based on integrated optical quantum platforms. Finally, we briefly describe other quantum algorithms and corresponding experimental implementations. These quantum computing schemes provide computational speedups for specific physical problems. In the future, with the further development of integrated optical quantum technology, along with the increase in the number of controllable qubits and the realization of the supporting quantum error correction system, a larger-scale many-body physical system can be constructed to further expand these algorithms and move towards the field of optical quantum computing, a new stage.
Quantum computing, based on the inherent superposition and entanglement properties of quantum states, can break through the limits of classical computing power. However, under the present technical conditions, the number of qubits that can be manipulated is still limited. In addition, the preparation of high-precision quantum gates and additional quantum error correction systems requires more auxiliary bits, which leads to extra cost. Therefore, it seems to be a long-term goal to realize a universal fault-tolerant quantum computer. The development of analog quantum computing is a transition path that can be used to simulate many-body physics problems. Quantum walk, as the quantum counterpart of classical random walks, is a research hotspot in analog quantum computing. Owing to the unique quantum superposition characteristics, quantum walk exhibits the ballistic transport properties of outward diffusion, so quantum walk provides acceleration in computing power for various algorithms. Based on quantum walk, different computing models are derived to deal with practical physical problems in different fields, such as biology, physics, economics, and computer science. A large number of technical routes are devoted to the experiments on realizing quantum walk, including optical fiber networks, superconducting systems, nuclear magnetic resonance systems, and trapped ion atom systems. Among these routes, photons are considered as the reliable information carriers in the experiments on quantum walking due to their controllability, long coherence time. and fast speed. Therefore, in this review, we focus on different quantum walk theories and experimental implementations in optical versions, such as traditional optical platforms, optical fiber platforms, and integrated optical quantum platform. In recent years, the rapid development of integrated optical quantum platforms has driven the experiments on quantum walk to move towards the stage of integration and miniaturization, and at the same time, the experimental scale and the number of qubits have gradually increased. To this end, we summarize the technological progress of integrated optical quantum computing, including various integrated optical quantum experimental platforms and their applications. Secondly, we specifically discuss the experiment on quantum walk and practical applications based on integrated optical quantum platforms. Finally, we briefly describe other quantum algorithms and corresponding experimental implementations. These quantum computing schemes provide computational speedups for specific physical problems. In the future, with the further development of integrated optical quantum technology, along with the increase in the number of controllable qubits and the realization of the supporting quantum error correction system, a larger-scale many-body physical system can be constructed to further expand these algorithms and move towards the field of optical quantum computing, a new stage.
Quantum computation exploits superposition, entanglement, interference, and measurement to process information.Research has moved from theoretical algorithms to functional devices where the performance is not only limited by qubit physics, but also by the control, compilation, error mitigation, and error correction.This review considers quantum computing on various levels of abstraction, including physical qubits, quantum algorithms, system architecture, fault-tolerance, and emerging applications.Study-specific evidence regarding scale, operational quality, connectivity, control and suppression of errors is compared across the different types of superconducting circuits, trapped ions, semiconductor spin qubits, neutral atoms and photonic processors.The review of the fundamental algorithms, noisy intermediate-scale quantum computing, variational and hybrid quantum-classical approaches, quantum simulation, quantum machine learning, and security implications is also discussed.Measurable progress has been demonstrated in published experiments for logical-qubit operation, below-threshold surface-code memory, programmable photonic sampling and platformspecific scaling.However, the reported metrics are not directly interchangeable, and practical advantage remains task-and baseline-dependent.Further progress will need to be accompanied by coordinated developments in hardware reliability, error correction, classical control, estimates of algorithmic resources, and clear classical method comparisons.
Quantum computing is an emerging computational paradigm that uses the principles of quantum mechanics, including superposition, entanglement, and quantum interference, to process information in fundamentally different ways from classical computers. During the last decade, quantum computing has progressed from theoretical research toward experimental and cloud-accessible systems. Recent developments have focused on increasing the number and quality of qubits, reducing gate errors, developing quantum error correction, improving quantum software, and integrating quantum processors with classical high-performance computing systems. Despite significant progress, practical large-scale quantum computing remains challenging because quantum states are extremely sensitive to environmental disturbances, resulting in decoherence and computational errors. Other challenges include limited qubit coherence time, gate fidelity, scalability, cryogenic requirements, quantum error correction overhead, hardware connectivity, programming complexity, and the difficulty of demonstrating useful quantum advantage for real-world applications. Recent developments from major research organizations indicate a shift from simply increasing physical qubit counts toward building reliable logical qubits and fault-tolerant quantum systems. Google has reported progress toward error-corrected logical qubits, while IBM\\\'s roadmap focuses on increasingly complex processors, modular architectures, quantum-classical integration, and fault-tolerant systems. Microsoft is pursuing a topological-qubit approach aimed at scalable quantum computing. This paper reviews the current state of quantum computing, examines major technological and scientific challenges, analyzes current research directions, and identifies future opportunities in drug discovery, materials science, optimization, cryptography, artificial intelligence, finance, and scientific simulation. The study concludes that the most realistic future is likely to involve quantum-centric computing, in which quantum processors work together with classical CPUs, GPUs, high-performance computing systems, and quantum networks rather than completely replacing classical computers.
Quantum computing represents a transformative paradigm that exploits the principles of quantum mechanics—superposition, entanglement, and quantum interference—to perform computations beyond the capabilities of classical computers. This review presents a comprehensive overview of the fundamental concepts underlying quantum computation, including qubits, quantum gates, quantum algorithms and measurement theory. It further examines major physical implementations of quantum computers, such as superconducting circuits, trapped ions, photonic systems, spin-based qubits, and topological approaches, highlighting their operational principles, advantages, and technological limitations. Key challenges hindering large-scale quantum computation—decoherence, error rates, qubit scalability, and fault tolerance—are critically discussed. Additionally, recent progress in quantum error correction, hybrid quantum–classical algorithms, and hardware optimization is summarized. The review concludes by outlining future research directions and potential applications of quantum computing in cryptography, materials science, optimization, and drug discovery. By integrating theoretical foundations with experimental advancements, this article aims to provide a clear and accessible reference for students, researchers, and scientists entering the rapidly evolving field of quantum computing.
Although practical realisation of a fully functioning quantum computer is still a long way off, recent progress both experimentally and theoretically is paving the way for possible implementations. One of the leading approaches is quantum optics, where photons are used as carriers of quantum information, and are manipulated in both linear and non-linear optical circuits. More recently, advances in integrated quantum photonics are enabling the realisation of compact and stable quantum gates and circuits. This approach provides routes to enhancing the complexity of quantum optic experiments, and ultimately towards the development of advanced quantum technologies. This study presents an overview of recent developments in the field of integrated waveguide quantum circuits. Key building blocks required for the realisation of quantum circuits are presented, and a rudimentary version of Shor's quantum factoring algorithm is demonstrated. Planar waveguide quantum circuits provide a high-performance platform from which quantum technologies and experimental quantum physics using single photons can be developed, and a new generation of quantum information and computing devices can be monolithically integrated onto a single optical chip.
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.
The classical random walk formalism plays an important role in a wide range of applications. Its quantum counterpart, the quantum walk, is proposed as an important theoretical model for quantum computing. By exploiting quantum effects such as superposition, interference, and entanglement, quantum walks and their variations have been extensively studied for achieving computing power beyond that of classical computing and have been broadly used in designing quantum algorithms for algebraic and optimization problems, graph and network analysis, and quantum Hamiltonian and biochemical process simulations. Moreover, quantum walk models have been proven capable of universal quantum computation. Unlike conventional quantum circuit models, quantum walks provide a feasible path for implementing application-specific quantum computing, particularly in the noisy intermediate-scale quantum era. Recently, remarkable progress has been achieved in implementing a wide variety of quantum walks and quantum walk applications, which demonstrates the great potential of quantum walks. In this review, we provide a thorough summary of quantum walks and quantum walk computing, including theories and characteristics, physical implementations, and applications. We also discuss the challenges facing quantum walk computing, which aims to realize a practical quantum computer in the near future.