量子计算的表象无关性探讨

大因数分解和数据检索量子算法的提出带来了量子计算与量子信息的研究高潮。由于量子计算具有并行性、不可克隆性及量子态的不可测性,使得量子信息及量子计算在某些方面具有传统计算所无法比拟的优势。量子的态空间作为一个完备的Hilbert空间,在定义了内积和范数并赋予相应的物理意义后,就构成了理论意义上的量子计算系统。该文抽象了量子系统的本质,描述了量子计算及遵循的计算规则以及如何实现量子信息表示和进行信息的处理与测量,从理论上阐述了量子态系统迁移的线性同构和等距同构,说明了量子计算与量子信息的研究与具体的量子表象空间无关。     

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。     

ScaffCC: Scalable compilation and analysis of quantum programs

We present ScaffCC, a scalable compilation and analysis framework based on LLVM (Lattner and Adve, 2004), which can be used for compiling quantum computing applications at the logical level. Drawing upon mature compiler technologies, we discuss similarities and differences between compilation of classical and quantum programs, and adapt our methods to optimizing the compilation time and output for the quantum case. Our work also integrates a reversible-logic synthesis tool in the compiler to facilitate coding of quantum circuits. Lastly, we present some useful quantum program analysis scenarios and discuss their implications, specifically with an elaborate discussion of timing analysis for critical path estimation. Our work focuses on bridging the gap between high-level quantum algorithm specifications and low-level physical implementations, while providing good scalability to larger and more interesting problems.     

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。     

Service differentiation and fair sharing in distributed quantum computing

In the future, quantum computers will become widespread and a network of quantum repeaters will provide them with end-to-end entanglement of remote quantum bits. As a result, a pervasive quantum computation infrastructure will emerge, which will unlock several novel applications, including distributed quantum computing, that is the pooling of resources on multiple computation nodes to address problem instances that are unattainable by any individual quantum computer. In this paper, we first investigate the issue of service differentiation in this new environment. Then, we define the problem of how to select which computation nodes should participate in each pool, so as to achieve a fair share of the quantum network resources available. The analysis is performed via an open source simulator and the results are fully and readily available.     

VENUS: A Geometrical Representation for Quantum State Visualization

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well-designed case studies and an in-depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits.     

单层量子感知器网络及其性能

目前人工智能的体系结构普遍比较复杂,所以它的广泛应用受到了很大的限制.利用量子计算的一些优点特别是量子并行计算特性提出一个单层量子感知器网络,该网络充分利用量子相位,使得它具有传统的单层感知器所无法具有的计算能力.对单层量子感知器进行实例分析、性能分析和仿真实验,表明单神经元量子感知器能实现单神经元经典感知器无法实现的XOR功能.即简单的网络结构实现了相对复杂的网络功能,这一特点有利于降低网络体系结构的复杂性,它必将对人工智能和控制领域的研究产生重大的影响.     

一种改进的混合量子遗传算法

提出了一种改进的混合量子遗传算法(IHQGA),该算法首先在量子个体上实施量子交叉,这一操作有利于保留相对较好的基因段;其次,采用量子比特相位法更新量子门和自适应调整搜索网格的策略;最后,引入拟Newton算法进行局部搜索操作,使得种群的多样性强,解得的收敛精度高,收敛速度快;通过复杂函数测试标明此算法的优化质量和效率都强于传统遗传算法和量子遗传算法;另外,从理论上也证明了该算法以概率l收敛于全局最优解.     

SHOR量子算法的原理与程序模拟

本文首先介绍大数质因子分解的Shor量子算法的原理、实现步骤和实现方法，然后用现存的模拟器在常规计算机上加以模拟。最后讨论了Shor算法模拟的意义，并对量子计算提出了看法。     

求列表极小值的量子算法

求列表极小值的算法具有广泛的应用。如果能够找到有效的求列表极小值的量子算法,那就可以找到求列表极大值的量子算法,从而与Grover量子搜索算法、求中值量子算法一起构成一套有效的量子算法体系。这些算法将构成用量子计算求解实际应用问题的核心和基础,并为量子算法的进一步研究提供坚实的基础。该文给出了一个时间复杂度为O(N√)的求列表极小值的量子算法。     

实数编码量子进化算法

为求解复杂函数优化问题,基于量子计算的相关概念和原理,提出一种实数编码量子进化算法.首先构造了由自变量向量的一个分量和量子比特的一对概率幅为等位基因的三倍体染色体,增加了解的多样性;然后利用量子旋转门和依据量子比特概率幅满足归一化条件设计的互补双变异算子进化染色体,实现局部搜索和全局搜索的平衡.标准函数仿真表明,该算法适合求解复杂函数优化问题,具有收敛速度快、全局搜索能力强和稳定性好的优点.     

基于量子蚁群算法的建筑消防疏散路径规划

摘要：针对现在大空间建筑消防应急疏散问题,在火灾发生时,为撤离人群提供一条从危险区域到安全地带的最短安全路线。对疏散路径优化进行了研究,提出一种融合量子进化算法的改进蚁群算法用于消防疏散路径规划,用量子比特表示信息素,量子旋转门反馈控制信息素更新,即能体现量子并行计算的高效性,又能拥有蚁群算法较好的寻优能力。通过三个基准函数优化仿真与传统量子进化算法进行对比,证明算法较优的性能。再通过路径优化的仿真实验与经典蚁群算法进行比较,结果表明,算法能够有效避免陷入局部最优和拥有更快的收敛速度,在疏散路径规划中更为有效。     

基于参数化量子电路的量子卷积神经网络模型及应用

量子神经网络结合了量子计算与经典神经网络模型的各自优势, 为人工智能领域的未来发展提供了一种 全新的思路. 本文提出一种基于参数化量子电路的量子卷积神经网络模型, 能够针对欧几里得结构数据与非欧几里 得结构数据, 利用量子系统的计算优势加速经典机器学习任务. 在MNIST数据集上的数值仿真结果表明, 该模型具 有较强的学习能力和良好的泛化性能.     

A quantum Jensen–Shannon graph kernel for unattributed graphs

In this paper, we use the quantum Jensen–Shannon divergence as a means of measuring the information theoretic dissimilarity of graphs and thus develop a novel graph kernel. In quantum mechanics, the quantum Jensen–Shannon divergence can be used to measure the dissimilarity of quantum systems specified in terms of their density matrices. We commence by computing the density matrix associated with a continuous-time quantum walk over each graph being compared. In particular, we adopt the closed form solution of the density matrix introduced in Rossi et al. (2013) 27 and 28 to reduce the computational complexity and to avoid the cumbersome task of simulating the quantum walk evolution explicitly. Next, we compare the mixed states represented by the density matrices using the quantum Jensen–Shannon divergence. With the quantum states for a pair of graphs described by their density matrices to hand, the quantum graph kernel between the pair of graphs is defined using the quantum Jensen–Shannon divergence between the graph density matrices. We evaluate the performance of our kernel on several standard graph datasets from both bioinformatics and computer vision. The experimental results demonstrate the effectiveness of the proposed quantum graph kernel.     

0/1背包问题的量子算法

近年来针对各种问题提出了许多量子算法,这些量子算法都利用了量子态的可迭加性(Superposition)和纠缠性(Entan-glement),本文在量子环境下对0/1背包问题进行求解,介绍了量子算法的基本思想及相关概念。然后分析并给出求解0/1背包问题的量子算法,在量子物理环境下它能在多项式时间内求出所需要的解。这个量子算法可以推广解决其它NPC问题,如旅行售货员问题等。     

量子算法模拟系统研究现状

The Quantum Computer has immense power,exceeds the capabilities of a classical computer,but the hardware of such machine is still in research. If we want to develop quantum algorithms,wemust simulate them on classical computer. In this paper ,we first introduce the principle and model usedin quantum computing,and compare the simulators in tile world. At last ,based on the problems in simu-lation,we give a new architecture of quantum algorithm simulator.     

量子协同免疫动态优化算法

基于协同策略和量子免疫计算理论,提出量子协同免疫动态优化算法,并从理论上证明算法的全局收敛性.该算法采用量子比特编码表达种群中的抗体,并采用量子旋转门和动态调整旋转步长策略来演化抗体,加速原有克隆算子的收敛.该算法中引入协同策略增强子群体间的信息交流,提高种群的多样性,同时利用量子编码种群的关联性,使算法具有更强的稳定性,能够较好地适应于动态问题的求解.文中通过一系列动态背包测试问题和交叉验证(t检验)实验表明,量子协同免疫动态优化算法具有更强的鲁棒性和适应性,显示出较优越的性能.     

SAR image segmentation based on quantum-inspired multiobjective evolutionary clustering algorithm

The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory result to one kind of corresponding data set. In this letter, a novel multiobjective clustering approach, named a quantum-inspired multiobjective evolutionary clustering algorithm (QMEC), is proposed to deal with the problem of image segmentation, where two objectives are simultaneously optimized. Based on the concepts and principles of quantum computing, the multi-state quantum bits are used to represent individuals and quantum rotation gate strategy is used to update the probabilistic individuals. The proposed algorithm can take advantage of the multiobjective optimization mechanism and the superposition of quantum states, and therefore it has a good population diversity and search capabilities. Due to a set of nondominated solutions in multiobjective clustering problems, a simple heuristic method is adopted to select a preferred solution from the final Pareto front and the results show that a good image segmentation result is selected. Experiments on one simulated synthetic aperture radar (SAR) image and two real SAR images have shown the superiority of the QMEC over three other known algorithms.