Brüschweiler量子搜索算法的改进及其实验实现

量子计算与经典计算相比, 能够极大地提高运算速度, 解决一些经典计算不能解决或很难解决的问题. 对于在无序数据库中进行搜索这类问题, 可以用量子算法, 如Brüschweiler量子搜索算法来解决. 与经典算法相比, Brüschweiler量子算法能够指数次地提高搜索速度. 在Brüschweiler提出的算法中, 数据量子位和观测量子位(辅助量子位)是分开的, 属于不同的量子位. 通过研究, 对Brüschweiler算法作了改进, 使之不需要用辅助量子位, 就可以达到指数次提高搜索速度的目的. 改进后的Brüschweiler量子算法有利于简化实验的设计和实现过程. 同时还利用核磁共振实验, 演示了改进后的Brüschweiler量子算法的实现.     

Brǖschweiler量子搜索算法的改进及其实验实现

量子计算与经典计算相比 ,能够极大地提高运算速度 ,解决一些经典计算不能解决或很难解决的问题 .对于在无序数据库中进行搜索这类问题 ,可以用量子算法 ,如Br櫣ｓｃｈｗｅｉｌｅｒ量子搜索算法来解决 .与经典算法相比 ,Br櫣ｓｃｈｗｅｉｌｅｒ量子算法能够指数次地提高搜索速度 .在Br櫣ｓｃｈｗｅｉｌｅｒ提出的算法中 ,数据量子位和观测量子位 (辅助量子位 )是分开的 ,属于不同的量子位 .通过研究 ,对Br櫣ｓｃｈｗｅｉｌｅｒ算法作了改进 ,使之不需要用辅助量子位 ,就可以达到指数次提高搜索速度的目的 .改进后的Br櫣ｓｃｈｗｅｉｌｅｒ量子算法有利于简化实验的设计和实现过程 .同时还利用核磁共振实验 ,演示了改进后的Br櫣ｓｃｈｗｅｉｌｅｒ量子算法的实现. In recent years, quantum computing research has made big progress, which exploit quantum mechanical laws, such as interference, superposition and parallelism, to perform computing tasks. The most inducing thing is that the quantum computing can provide large rise to the speedup in quantum algorithm. Quantum computing can solve some problems, which are impossible or difficult for the classical computing. The problem of searching for a specific item in an unsorted database can be...     

量子网络中非局域Toffoli门的腔-QED实现方案

量子计算机能够解决经典计算机难以处理的问题，比如大数分解等。然而，在实际操作中，需要对足够多的量子位进行逻辑操作，而制备和操作大数量的量子位已经遇到了阻力；Grover提出了分布式量子计算的方案，构建由多个只包含少量量子位处理器组成的量子计算机，有效的解决了制备和操作大数量量子位问题。这种方案可以看作解决由传送者和接受者操纵包含多个由少量量子位组成的网点的量子网络问题。因此在分布式量子计算中利用量子纠缠通道，通过局域操作和经典通讯实现非局域的量子操作就显得非常有意义。我们这里给出了一种有效的简单非局域Toffoli门的腔-QED实现方案。     

量子计算加速的解法器算法及应用综述

量子计算作为一种基于量子力学原理的全新计算模型,具有强大的并行性和潜在的颠覆性影响力,为解决复杂问题提供了新的思路。本文的主要目标是对量子计算在大规模科学与工程计算领域中数值计算问题的解法器算法和应用进行综述。重点介绍量子计算在线性方程组、特征值问题、微分方程、哈密顿量与图计算、量子机器学习、量子解法器平台以及实际数值模拟等领域的具体应用。针对不同的数值计算问题,详细讨论当前主流的量子计算算法,并总结近年来国内外相关算法的研究进展。最后,对量子计算在数值计算求解相关研究方向的未来发展趋势进行展望。     

量子算法与量子计算实验

从量子体系的基本特性出发 ,介绍了量子计算的基本概念和物理背景 ,系统阐述了几种主要的量子算法以及量子计算在实验方面的发展现状。对比经典计算机 ,讨论了量子计算机的优越性、实现量子计算的困难和以期克服的途径。     

量子计算算法介绍

量子计算机利用量子力学原理进行计算,具有量子并行计算的优势,能够超越经典计算1990年中期,量子算法取得突破,舒尔(Shor)构造了大数质因子的量子算法,葛洛沃(Grover)构造了无序数据库的量子搜索算法,引起了人们对量子计算的重视,极大地推动了量子计算的研究.文章简单介绍了几个典型的量子算法以及量子算法研究的一些新进展.     

量子算法与量子计算实验

从量子体系的基本特性出发,介绍了量子计算的基本概念和物理背景,系统阐述了几种主要的最子算法以及量子计算在实验方面的发展现状。 对比经典计算机,讨论了量子计算机的优越性、实现量子计算的困难和以期克服的途径。     

基于相位匹配的量子行走搜索算法及电路实现

量子行走是经典随机行走在量子力学框架下的对应, 理论上可以用来解决一类无序数据库的搜索问题. 因为携带信息的量子态的扩散速度与经典相比有二次方式的增长, 所以量子行走优于经典随机行走, 量子行走的特性值得加以利用. 量子行走作为一种新发现的物理现象的数学描述, 引发了一种新的思维方式, 孕育了一种新的理论计算模型. 最新研究表明, 量子行走本身也是一种通用计算模型, 可被视为设计量子算法的高级工具, 因此受到部分计算机理论科学领域学者的关注和研究. 对于多数问题求解方案的量子算法的设计, 理论上可以只在量子行走模型下进行考虑. 基于Grover算法的相位匹配条件, 本文提出了一个新的基于量子行走的搜索算法. 理论演算表明: 一般情况下本算法的时间复杂度与Grover算法相同, 但是当搜索的目标数目多于总数的1/3时, 本算法搜索成功的概率要大于Grover算法. 本文不但利用Grover算法中相位匹配条件构造了一个新的量子行走搜索算法, 而且在本研究室原有的量子电路设计研究成果的基础上给出了该算法的量子电路表述.     

固态量子计算的若干重要物理问题研究

量子计算机拥有比经典计算机更为强大的计算能力．人们普遍认为量子计算机最终将会在固态系统中实现．文章介绍了一些有关固态量子计算的研究进展，其中包括超导电荷量子比特方案、几何量子计算、量子点量子比特及量子计算若干基本问题研究．最后给出了固态量子计算的发展趋势．     

膜量子蜂群优化的多目标频谱分配

为了解决认知无线电系统中最大和网络效益和用户间公平性联合最优化的多目标频谱分配难题,基于量子蜂群理论和膜计算,提出了一种新的离散多目标组合优化算法—–膜量子蜂群优化.所提算法在基础膜可以搜索到单个目标的全局最优解,在表层膜获得兼顾网络效益和公平的Pareto前端解.通过膜间的通信规则、量子觅食行为的协同演进和非支配解排序可获得能同时求解单目标和多目标优化问题的多目标优化算法,并与经典的敏感图论着色算法、遗传算法、量子遗传算法和粒子群算法等频谱分配算法在不同的目标函数下进行仿真性能比较.仿真结果表明:在不同网络效益函数下所提的膜量子蜂群频谱分配算法都能够较好地找到单目标最优解,优于经典的频谱分配算法和已有的智能频谱分配算法,还可获得多目标频谱分配的Pareto前端最优解集.     

广义量子干涉原理及对偶量子计算机

我们综述最近提出的广义量子干涉原理及其在量子计算中的应用。广义量子干涉原理是对狄拉克单光子干涉原理的具体化和多光子推广,不但对像原子这样的紧致的量子力学体系适用,而且适用于几个独立的光子这样的松散量子体系。利用广义量子干涉原理,许多引起争议的问题都可以得到合理的解释,例如两个以上的单光子的干涉等问题。从广义量子干涉原理来看双光子或者多光子的干涉就是双光子和双光子自身的干涉,多光子和多光子自身的干涉。广义量子干涉原理可以利用多组分量子力学体系的广义Feynman积分表示,可以定量地计算。基于这个原理我们提出了一种新的计算机,波粒二象计算机,又称为对偶计算机。在原理上对偶计算机超越了经典的计算机和现有的量子计算机。在对偶计算机中,计算机的波函数被分成若干个子波并使其通过不同的路径,在这些路径上进行不同的量子计算门操作,而后这些子波重新合并产生干涉从而给出计算结果。除了量子计算机具有的量子平行性外,对偶计算机还具有对偶平行性。形象地说,对偶计算机是一台通过多狭缝的运动着的量子计算机,在不同的狭缝进行不同的量子操作,实现对偶平行性。目前已经建立起严格的对偶量子计算机的数学理论,为今后的进一步发展打下了基础。本文着重从物理的角度去综述广义量子干涉原理和对偶计算机。现在的研究已经证明,一台d狭缝的n比特的对偶计算机等同与一个n比特+一个d比特(qudit)的普通量子计算机,证明了对偶计算机具有比量子计算机更强大的能力。这样,我们可以使用一台具有n+log2d个比特的普通量子计算机去模拟一个d狭缝的n比特对偶计算机,省去了研制运动量子计算机的巨大的技术上的障碍。我们把这种量子计算机的运行模式称为对偶计算模式,或简称为对偶模式。利用这一联系反过来可以帮助我们理解广义量子干涉原理,因为在量子计算机中一切计算都是普通的量子力学所允许的量子操作,因此广义量子干涉原理就是普通的量子力学体系所允许的原理,而这个原理只是是在多体量子力学体系中才会表现出来。对偶计算机是一种新式的计算机,里面有许多问题期待研究和发展,同时也充满了机会。在对偶计算机中,除了幺正操作外,还可以允许非幺正操作,几乎包括我们可以想到的任何操作,我们称之为对偶门操作或者广义量子门操作。目前这已经引起了数学家的注意,并给出了广义量子门操作的一些数学性质。此外,利用量子计算机和对偶计算机的联系,可以将许多经典计算机的算法移植到量子计算机中,经过改造成为量子算法。由于对偶计算机中的演化是非幺正的,对偶量子计算机将可能在开放量子力学的体系的研究中起到重要的作用。     

Duality Computing in Quantum Computers

In this letter, we propose a duality computing mode, which resembles particle-wave duality property when a quantum system such as a quantum computer passes through a double-slit. In this mode, computing operations are not necessarily unitary. The duality mode provides a natural link between classical computing and quantum computing. In addition, the duality mode provides a new tool for quantum algorithm design.     

A prototype of quantum von Neumann architecture

A modern computer system, based on the von Neumann architecture, is a complicated system with several interactive modular parts. It requires a thorough understanding of the physics of information storage, processing, protection, readout, etc. Quantum computing, as the most generic usage of quantum information, follows a hybrid architecture so far, namely, quantum algorithms are stored and controlled classically, and mainly the executions of them are quantum, leading to the so-called quantum processing units. Such a quantum–classical hybrid is constrained by its classical ingredients, and cannot reveal the computational power of a fully quantum computer system as conceived from the beginning of the field. Recently, the nature of quantum information has been further recognized, such as the no-programming and no-control theorems, and the unifying understandings of quantum algorithms and computing models. As a result, in this work, we propose a model of a universal quantum computer system, the quantum version of the von Neumann architecture. It uses ebits (i.e. Bell states) as elements of the quantum memory unit, and qubits as elements of the quantum control unit and processing unit. As a digital quantum system, its global configurations can be viewed as tensor-network states. Its universality is proved by the capability to execute quantum algorithms based on a program composition scheme via a universal quantum gate teleportation. It is also protected by the uncertainty principle, the fundamental law of quantum information, making it quantum-secure and distinct from the classical case. In particular, we introduce a few variants of quantum circuits, including the tailed, nested, and topological ones, to characterize the roles of quantum memory and control, which could also be of independent interest in other contexts. In all, our primary study demonstrates the manifold power of quantum information and paves the way for the creation of quantum computer systems in the near future.     

Optically induced phase transition of excitons in coupled quantum dots

The weak classical light excitations in many semiconductor quantum dots have been chosen as important solid- state quantum systems for processing quantum information and implementing quantum computing. For strong classical light we predict theoretically a novel phase transition as a function of magnitude of this classical light from the deformed to the normal phases in resonance ease, and the essential features of criticality such as the scaling behaviour, critical exponent and universality are also present in this paper.     

Universal Distributed Quantum Computing on Superconducting Qutrits with Dark Photons

A one‐step scheme is presented to construct the controlled‐phase gate deterministically on remote transmon qutrits coupled to different resonators connected by a superconducting transmission line for an universal distributed quantum computing. Different from previous work on remote superconducting qubits, the present gate is implemented with coherent evolutions of the entire system in the all‐resonance regime assisted by the dark photons to robust against the transmission line loss, which allows the possibility of the complex designation of a long‐length transmission line to link lots of circuit QEDs. The length of the transmission line can reach the scale of several meters, which makes this scheme suitable for large‐scale distributed quantum computing. This gate is a fast quantum entangling operation with a high fidelity of about 99%. Compared with previous work in other quantum systems for a distributed quantum computing, under the all‐resonance regime, the present proposal does not require classical pulses and ancillary qubits, which relaxes the difficulty of its implementation largely.     

Realizing number recognition with simulated quantum semi-restricted Boltzmann machine

Quantum machine learning based on quantum algorithms may achieve an exponential speedup over classical algorithms in dealing with some problems such as clustering. In this paper, we use the method of training the lower bound of the average log likelihood function on the quantum Boltzmann machine (QBM) to recognize the handwritten number datasets and compare the training results with classical models. We find that, when the QBM is semi-restricted, the training results get better with fewer computing resources. This shows that it is necessary to design a targeted algorithm to speed up computation and save resources.     

固态量子计算

量子计算机拥有比经典计算机更强大的计算能力，人们普遍认为，量子计算机最终将会在固态系统中实现，文章介绍了三种固态量子计算机的方案，它们分别基于固态核振磁共振，超导结和量子点。     

量子信息与计算

量子信息与计算是物理学目前研究的热门领域 .本文简要地介绍量子计算的一些基本概念 :量子纠缠、量子位、量子寄存器、量子并行计算和量子纠错 .并介绍两种典型的量子信息技术 :量子密码和量子传物 .     

The Large Hadron Collider: A Marvel of Technology,edited by Lyndon Evans

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.