Distributed quantum computing with trapped ions is a quantum computing model which takes intermediate-sized trapped ion processors as computing nodes, and connects each computing node through ion-photon entanglement. It is expected to obtain a wide range of application scenarios, such as solving problems computationally demanding in the fields of finance, chemistry, cryptography, and so on, breaking through the limitations of existing methods in these fields. This paper briefly introduces the principle and current status of distributed trapped-ion quantum computing, the possible direction of future development, and application scenarios.
Quantum computing has the potential to surpass classical computing. In recent years, there have been significant advances in technology research, application exploration, and industry ecosystem cultivation. Overall development has entered the fast track and has become a hot spot for scientific research layout and investment in many countries worldwide. This paper focuses on summarizing and analyzing the progress of key technologies of quantum computing, the trends of application exploration, and industry ecosystem cultivation, with a view towards future development trends.
Quantum computing is expected to provide exponential speedup for specific NP-hard problems, which becomes a hotspot of academic research and industrial development. The trapped-ion system is one of the most mature platforms to achieve large-scale universal quantum computing. First, the basic idea and principles of trapped-ion quantum computing are introduced. Then, the advantages, disadvantages and the strategies to scale up are also carefully discussed. Finally, there is a brief survey regarding the recent status of commercialized development and applications of trapped-ion quantum computing.
Software and hardware of quantum computer are still immature, which can hardly meet the requirements of local deployment for individual users due to strict environment requirements and high operation and maintenance costs. The cloud platform has become an ideal test bed for quantum computing science popularization, algorithm development, and application innovation. Based on the current development status and service mode classification of typical quantum computing cloud platforms, a universal functional architecture model is proposed. The analysis on the prospects of quantum computing platforms is conducted, and suggestions for future development to overcome main challenges are also discussed.
By combining quantum computing with classical computing, the potential of quantum devices at the current stage can be fully exploited to speed up the computation process. In addition, the characteristics of quantum computing determine that even to achieve the fault-tolerant quantum computing stage, it still needs the cooperation of classical computing. This article provides a concise exposition of the basic framework and subsequent key technical challenges of hybrid quantum-classical computing (HQC) based on the HQC proposals put forward by researchers both domestically and internationally, as well as the quantum operating systems (OS) already released by various enterprises. It also looks ahead to the future development prospects of HQC in China.
Integrated quantum memory plays a crucial role in large-scale quantum networks. Over the past years, rare-earth ions doped solid-state materials have shown significant advantages in achieving high-performance integrated photonic quantum memory. This kind of photonic quantum memory enables photonic quantum storage to have long storage time, high efficiency, high fidelity, large bandwidth and multimode capacity, and can also be integrated with other quantum functional devices. This paper reviews progress of integrated photonics quantum memories with various devices fabricated in rare-earth ions doped solid-state materials, such as erbium doped silica fibers, titanium-indiffused LiNbO3 waveguides, femtosecond laser micromachining waveguides, and focused ions beam etching photonic devices. It also discusses the merits and potential of those photonics quantum memories and the challenges.
Continuous-variable quantum key distribution using coherent states is an important means of quantum secure communications. It usually realizes key distillation with theoretical unconditional security through the preparation, transmission and measurement of coherent states. It has the advantages of high compatibility with coherent optics communications, high secret key rate within metropolitan area, supporting point-to-multipoint key distribution, and is suitable for large-scale deployment in metropolitan and access networks. This perspective article reviews the protocols, security theory, systematization, and networking development status of continuous-variable quantum key distribution, sorts out the key technologies used in the application scenarios of metropolitan and access networks, and looks forward to its future development trend.
Quantum Information Network (QIN) aims to connect multiple quantum processor nodes, providing interconnection networking capabilities for quantum information systems such as quantum computers and quantum sensors. It is an important direction for the future development and integration of quantum information technology. This article reviewed key technical principles, enabling building blocks, and future technical development concerns of the QIN, providing references for research and application exploration in related fields.
The quantum magnetic field measurement technology can help to achieve leapfrog development of magnetic field measurement sensitivity, and has become a research hotspot in various countries around the world in recent years. First, the development of quantum magnetic field measurement technology is briefly described. Then, its application in the field of biological magnetic field imaging is introduced. Finally, the industrial status and future development trends of the two important application directions (magnetocardiography and magnetoencephalography) of quantum magnetic field measurement technology are analyzed.
In the current development process of information technology, open source software and standards are deeply integrated and complement each other’s strengths, coordinating to promote technological innovation. At the same time, the combination of open source software and standards has raised concerns about intellectual property conflicts. This paper first compares the differences between open source software and standards. Then, it analyzes the model coordinating open source software and standards as well as the advantages. On this basis, it elaborates on the possible intellectual property issues in the coordinated development of open source software and standards in two aspects: intellectual property policy conflicts and intellectual property risks.
Recently, generative Artificial Intelligence (AI) has been developing rapidly, becoming the focus of society. While promoting a revolution in related industries, it also causes personal information protection risks due to technology abuse. By analyzing the problems caused by generative AI and reviewing existing policies at home and abroad, this paper proposes possible governance suggestions in the aspects of concepts, compliance systems, technical infrastructure, and service guarantee, with balancing development and regulation as the core.
With the emergence of new services and the continuous evolution of IP network technology, cloud-network convergence has entered a new stage, showing the development characteristics of digitization, intelligence and servitization. Intelligence needs to be combined with relevant artificial intelligence technologies.Deep learning and deep reinforcement learning are commonly used artificial intelligence algorithms. With the development of graph neural network and other technologies, the ability of deep learning to represent graph information and the ability of deep reinforcement learning to deal with optimization problems have been improved. IP networks can be represented abstractly by using graph structures, and related prediction and optimization problems can be processed and solved by using deep learning and deep reinforcement learning algorithms. Therefore, this paper describes the related algorithms and applications of deep learning and deep reinforcement learning in three scenarios including traffic prediction, network planning and traffic engineering, and analyzes the possible problems and challenges that may occur in practice.