新量子技术时代下的信息安全

量子技术将在未来深刻影响密码学以及信息安全行业。可以利用上千个量子比特运行量子算法的通用量子计算机将直接威胁信息安全基础算法,导致当前广泛使用的RSA等公钥密码被破解,也会使分组密码算法的密码强度减半。量子通信中量子密钥分发的实施会改变传统保密通信的物理结构。这些重大 应用价值也是发展量子技术的驱动力。结合当前一些关于量子技术的热点新闻,从量子计算和量子通信两个方面分别综述了量子技术对信息安全技术的影响。同时简要介绍了这些技术的最新发展现状,包括通用型和专用型量子计算机的发展、量子密钥分发技术实验室环境的进展以及天地一体化量子通信网络的发展状况等。最后对信息安全技术的未来形态做了思考和总结。未来量子技术将会与现有各种技术深度融合,共同存在。     

量子理论在信息安全领域的应用前景

由于量子的特性，许多问题采用经典计算机只能进行指数算法，而量子计算机能用多项式算法来完成，如采用量子计算机可以在多项式时间内进行大数因子分解，因而对于现有的密码体制构成威胁。量子密码学以及量子隐形传态可以进行保密通信，窃听者不可能得到信息，并且合法用户会发现窃听的存在，这些是现在的密码体制所不可以比拟的。     

量子计算机:量子算法与物理实现

量子算法与物理实现是量子计算机研究中的两个基本问题。本文首先总结了相关领域的主要进展,并讨论了有代表性的量子算法,特别介绍了用于求解线性方程组的量子算法,分析了影响新量子算法提出的因素。然后,探讨了物理实现的迪文森佐判据,并介绍了典型的实现方案及性能比较。同时,也关注了对量子计算机研究持有异议的观点。最后,对量子计算机的新研究方向作了探讨。     

量子计算与量子计算机

量子计算是一种依照量子力学理论进行的新型计算,量子计算的基础和原理以及重要量子算法为在计算速度上超越图灵机模型提供了可能。在发展与完善量子计算理论的同时,量子计算机的物理实现方案也被不断提出。光子量子计算机,基于核磁共振、离子阱或谐振子等技术的量子计算机物理模型已被逐一实现。近年来亦出现了几个典型的基于量子计算机的量子算法。2001年在一台基于核磁共振技术的量子计算设备上成功演示的Shor量子算法,显示出量子计算机处理复杂问题的巨大潜能。文章对当前量子计算机物理实现的研究进展进行了综述。     

量了计算与量子计算机

最子计算的强大运算使得量子计算机具有广泛的应用前景，该文简要介绍了量子计算的发展现状和基本原理，玩举了典型的量子算法，阐明了量子计算机的优越性，最后预测了量子计算及量子计算机的应用方向。     

国外量子计算机进展 对信息安全的挑战与对策

对加拿大D-Wave公司的量子计算机进展和相关情况进行了分析,并针对量子计算机对于信息安全的挑战,讨论了抗量子计算的公钥密码领域的竞争态势和发展思路。     

量子算法在大数据时代的应用浅析

量子计算机的提出不仅在计算机领域,而且在物理、通信、材料等很多领域产生了巨大的反响,目前各国都加 入到量子计算机的研发中,量子算法、构建、物理实现等方面都有很多进展。随着信息技术的进步,人们对数据处理的需求和 速率要求变得日益苛刻,在这种背景下,量子技术与大数据处理技术的结合成为突破大数据处理的曙光。     

量子计算的研究与应用

对量子计算的最新研究方向进行了介绍,简述了量子计算和量子信息技术的重要应用领域。分析了量子计算机与经典计算机相比所具有的优点和目前制约量子计算机应用发展的主要因素,最后展望了其未来发展趋势。     

量子计算机的研究与应用

介绍了量子计算的最新研究方向,简述了量子计算和量子信息技术在保密通信、量子算法、数据库搜索等重要领域的应用。分析了量子计算机与经典计算机相比所具有的优点和目前制约量子计算机应用发展的主要因素,最后展望了其未来发展趋势。     

量子计算机与经典计算机的比较

本文分析了经典计算机和量子计算机的异同;介绍了量子计算机的原理和特点,指出量子计算和量子信息技术在并行计算、保密通信等方面的重要应用。     

量子小波变换算法及线路实现

随着小波理论研究的深入,以及小波分析在信号分析和图像处理等领域的广泛应用,小波分析在量子计算领域中也越来越受到重视.应用置换矩阵、W-H变换矩阵和量子傅立叶变换矩阵来对Haar小波及D(4)小波变换矩阵进行分解,给出其算法,然后得出其完整的量子逻辑线路图,最后分析其复杂度.     

Quantum estimation,control and learning: Opportunities and challenges

The development of estimation and control theories for quantum systems is a fundamental task for practical quantum technology. This vision article presents a brief introduction to challenging problems and potential opportunities in the emerging areas of quantum estimation, control and learning. The topics cover quantum state estimation, quantum parameter identification, quantum filtering, quantum open-loop control, quantum feedback control, machine learning for estimation and control of quantum systems, and quantum machine learning.     

Mathematical models of quantum computation

In this paper, we introduce two mathematical models of realistic quantum computation. First, we develop a theory of bulk quantum computation such as NMR (Nuclear Magnetic Resonance) quantum computation. For this purpose, we define bulk quantum Turing machine (BQTM for short) as a model of bulk quantum computation. Then, we define complexity classes EBQP, BBQP and ZBQP as counterparts of the quantum complexity classes EQP, BQP and ZQP, respectively, and show that EBQP=EQP, BBQP=BQP and ZBQP=ZQP. This implies that BQTMs are polynomially related to ordinary QTMs as long as they are used to solve decision problems. We also show that these two types of QTMs are also polynomially related when they solve a function problem which has a unique solution. Furthermore, we show that BQTMs can solve certain instances of NP-complete problems efficiently. On the other hand, in the theory of quantum computation, only feed-forward quantum circuits are investigated, because a quantum circuit represents a sequence of applications of time evolution operators. But, if a quantum computer is a physical device where the gates are interactions controlled by a current computer such as laser pulses on trapped ions, NMR and most implementation proposals, it is natural to describe quantum circuits as ones that have feedback loops if we want to visualize the total amount of the necessary hardware. For this purpose, we introduce a quantum recurrent circuit model, which is a quantum circuit with feedback loops. LetC be a quantum recurrent circuit which solves the satisfiability problem for a blackbox Boolean function includingn variables with probability at least 1/2. And lets be the size ofC (i.e. the number of the gates inC) andt be the number of iterations that is needed forC to solve the satisfiability problem. Then, we show that, for those quantum recurrent circuits, the minimum value ofmax(s, t) isO(n ²2 ^n/3). Tetsuro Nishino, D.Sc.: He is presently an Associate Professor in the Department of Information and Communication Engineering, The University of Electro-Communications. He received the B.S., M.S. and D.Sc degrees in mathematics from Waseda University, in 1982, 1984 and 1991 respectively. From 1984 to 1987, he joined Tokyo Research Laboratory, IBM Japan. From 1987 to 1992, he was a Research Associate of Tokyo Denki University, and from 1992 to 1994, he was an Associate Professor of Japan Advanced Institute of Science and Technology, Hokuriku. His main interests are circuit complexity theory, computational learning theory and quantum complexity theory.     

Self-protected quantum algorithms based on quantum state tomography

Only a few classes of quantum algorithms are known which provide a speed-up over classical algorithms. However, these and any new quantum algorithms provide important motivation for the development of quantum computers. In this article new quantum algorithms are given which are based on quantum state tomography. These include an algorithm for the calculation of several quantum mechanical expectation values and an algorithm for the determination of polynomial factors. These quantum algorithms are important in their own right. However, it is remarkable that these quantum algorithms are immune to a large class of errors. We describe these algorithms and provide conditions for immunity.      

A Survey on quantum computing technology

The power of quantum computing technologies is based on the fundamentals of quantum mechanics, such as quantum superposition, quantum entanglement, or the no-cloning theorem. Since these phenomena have no classical analogue, similar results cannot be achieved within the framework of traditional computing. The experimental insights of quantum computing technologies have already been demonstrated, and several studies are in progress. Here we review the most recent results of quantum computation technology and address the open problems of the field.     

量子力学和量子计算机

In this paper,the relationship between computation and physics and the application of the principle of Quantum mechanics to Quantum Computing and Quantum Computers was reviewed     

Optimal computation with non-unitary quantum walks

Quantum versions of random walks on the line and the cycle show a quadratic improvement over classical random walks in their spreading rates and mixing times, respectively. Non-unitary quantum walks can provide a useful optimisation of these properties, producing a more uniform distribution on the line, and faster mixing times on the cycle. We investigate the interplay between quantum and random dynamics by comparing the resources required, and examining numerically how the level of quantum correlations varies during the walk. We show numerically that the optimal non-unitary quantum walk proceeds such that the quantum correlations are nearly all removed at the point of the final measurement. This requires only O(logT)$O(logT)$ random bits for a quantum walk of T$T$ steps.     

Combating Quantum Burst-errors with Quantum Interleaving Technique

Based on the interleaving technique, a kn-qubit code is constructed in this paper with more error-correcting ability than one n-qubit quantum error-correcting code without introducing the redundant qubits. By converting quantum bursts of errors into quantum random errors with the help of the quantum interleaving of the several states of the same quantum code, the proposed technique becomes an effective means to combat quantum bursts of errors. It is much simple and applicable for the quantum interleaving techniques to be used in the optical-fiber communications.     

Quantum covariance and filtering

We give a tutorial exposition of the analogue of the filtering equation for quantum systems focusing on the quantum probabilistic framework and developing the ideas from the classical theory. Quantum covariances and conditional expectations on von Neumann algebras play an essential part in the presentation.