Quantum computers and quantum coherence

If the states of spins in solids can be created, manipulated, and measured at the single-quantum level, an entirely new form of information processing, quantum computing, will be possible. We first give an overview of quantum information processing, showing that the famous Shor speedup of integer factoring is just one of a host of important applications for qubits, including cryptography, counterfeit protection, channel capacity enhancement, distributed computing, and others. We review our proposed spin-quantum dot architecture for a quantum computer, and we indicate a variety of first generation materials, optical, and electrical measurements which should be considered. We analyze the efficiency of a two-dot device as a transmitter of quantum information via the propagation of qubit carriers (i.e. electrons) in a Fermi sea.     

Photonic quantum information processing

Information processing with light is ubiquitous, from communication, metrology and imaging to computing. When we consider light as a quantum mechanical object, new ways of information processing become possible. In this review I give an overview of how quantum information processing can be implemented with single photons, and what hurdles still need to be overcome to implement the various applications in practice. I will place special emphasis on the quantum mechanical properties of light that make it different from classical light, and how these properties relate to quantum information processing tasks.     

A driven three-dimensional electric lattice for polar molecules

Three-dimensional (3D) driven optical lattices have attained great attention for their wide applications in the quest to engineer new and exotic quantum phases. Here we propose a 3D driven electric lattice (3D-DEL) for cold polar molecules as a natural extension. Our 3D electric lattice is composed of a series of thin metal plates in which two-dimensional square hole arrays are distributed. When suitable modulated voltages are applied to these metal plates, a 3D potential well array for polar molecules can be generated and can move smoothly back and forth in the lattice. Thus, it can drive cold polar molecules confined in the 3D electric lattice. Theoretical analyses and trajectory calculations using two types of molecules, ND₃ and PbF, are performed to justify the possibility of our scheme. The 3D-DEL offers a platform for investigating cold molecules in periodic driven potentials, such as quantum computing science, quantum information processing, and some other possible applications amenable to the driven optical lattices.     

核磁共振量子信息处理研究的新进展

过去的二十年中,量子信息相关研究取得了显著的进展,重要的理论和实验工作不断涌现.与其他量子信息处理系统相比,基于自旋动力学的核磁共振系统,不仅具有丰富而且成熟的控制技术,还拥有相干时间长、脉冲操控精确、保真度高等优点.这也是核磁共振体量子系统能够精确操控多达12比特的量子系统的原因.因此,核磁共振量子处理器在量子信息领域一直扮演着重要角色.本文介绍核磁共振量子计算的基本原理和一些新研究进展.研究的新进展主要包括量子噪声注入技术、量子机器学习在核磁共振平台上的实验演示、高能物理和拓扑序的量子模拟以及核磁共振量子云平台等.最后讨论了液态核磁共振的发展前景和发展瓶颈,并对未来发展方向提出展望.     

When optical microscopy meets all-optical analog computing: A brief review

As a revolutionary observation tool in life science, biomedical, and material science, optical microscopy allows imaging of samples with high spatial resolution and a wide field of view. However, conventional microscopy methods are limited to single imaging and cannot accomplish real-time image processing. The edge detection, image enhancement and phase visualization schemes have attracted great interest with the rapid development of optical analog computing. The two main physical mechanisms that enable optical analog computing originate from two geometric phases: the spin-redirection Rytov-Vlasimirskii-Berry (RVB) phase and the Pancharatnam-Berry (PB) phase. Here, we review the basic principles and recent research progress of the RVB phase and PB phase based optical differentiators. Then we focus on the innovative and emerging applications of optical analog computing in microscopic imaging. Optical analog computing is accelerating the transformation of information processing from classical imaging to quantum techniques. Its intersection with optical microscopy opens opportunities for the development of versatile and compact optical microscopy systems.     

云计算技术在未来智能电网信息处理平台中的应用

未来的电网将会是一个高度自动化、智能化的电网,如此一个庞大、复杂的系统从发电、输电一直到变、配、用电的过程时刻都会生成海量的电能信息及设备运行状态数据,这就需要一个强有力的信息处理平台作为支撑,且这个平台应具有对海量数据的可靠存储、准确分析、实时同步更新及共享的功能,尤其是音视频等异构数据的分析与处理.云计算技术在大数据处理中具有得天独厚的优势,本文将云计算技术引入到未来智能电网的信息处理平台的建设中,针对未来智能电网信息处理平台的各项功能需求,对比目前电网信息处理平台的诸多瓶颈进行深层次的探讨,证明了将云计算技术应用到未来智能电网信息处理平台中的可行性。     

Optoelectronic memristor for neuromorphic computing

With the need of the internet of things,big data,and artificial intelligence,creating new computing architecture is greatly desired for handling data-intensive tasks.Human brain can simultaneously process and store information,which would reduce the power consumption while improve the efficiency of computing.Therefore,the development of brainlike intelligent device and the construction of brain-like computation are important breakthroughs in the field of artificial intelligence.Memristor,as the fourth fundamental circuit element,is an ideal synaptic simulator due to its integration of storage and processing characteristics,and very similar activities and the working mechanism to synapses among neurons which are the most numerous components of the brains.In particular,memristive synaptic devices with optoelectronic responding capability have the benefits of storing and processing transmitted optical signals with wide bandwidth,ultrafast data operation speed,low power consumption,and low cross-talk,which is important for building efficient brain-like computing networks.Herein,we review recent progresses in optoelectronic memristor for neuromorphic computing,including the optoelectronic memristive materials,working principles,applications,as well as the current challenges and the future development of the optoelectronic memristor.     

Electron Spins in Quantum Dots as Quantum Bits

The creation, coherent manipulation, and measurement of spins in nanostructures open up completely new possibilities for electronics and information processing, among them quantum computing and quantum communication. We review our theoretical proposal for using electron spins in quantum dots as quantum bits, explaining why this scheme satisfies all the essential requirements for quantum computing. We include a discussion of the recent measurements of surprisingly long spin coherence times in semiconductors. Quantum gate mechanisms in laterally and vertically tunnel-coupled quantum dots and methods for single-spin measurements are introduced. We discuss detection and transport of electronic EPR pairs in normal and superconducting systems.     

功能梯度材料研究的现状与将来发展

功能梯度材料是近年来材料科学的研究热点之一,文章评述了ＦＧＭ的基本概念、制备技术、理论研究和应用情况等发展现状,同时对可能的发展进行了讨论,强调了对电磁性能研究的重要性。     

磁子转矩翻转磁矩研究

磁子是自旋波量子化的准粒子。磁子具有在绝缘磁性材料中无热耗散、低阻尼、长距离传输自旋的优势,避免了因电荷流动而产生焦耳热,可以克服日益显著的器件发热问题,因此磁子器件在低功耗信息存储与计算领域具有潜在应用前景。文章首先介绍了自旋波和磁子的概念,磁子具有的优势和研究价值;然后总结了磁子在铁磁和反铁磁绝缘体中输运以及新型磁子器件方面的最新研究结果;最后详细介绍了室温下实现磁子转矩驱动磁矩翻转的最新研究工作。这些工作对发展磁子学,实现低功耗、高速磁子型器件及应用具有较为重要的现实意义。     

非线性有机材料DAST的研究及应用

随着光通信、光信息处理技术等的快速发展,非线性光学材料越来越受到学术界与工业的关注。与无机非线性光学材料相比,有机非线性光学材料具有响应时间短、易于加工和高非线性系数等优点。其中,4-(4-二甲氨基苯乙烯基)甲基吡啶对甲苯磺酸盐(DAST)是人工设计的一种具有非中心对称性和较强极化率的有机非线性材料。大量理论和实验结果表明,DAST具有高二阶光学非线性系数、高电光系数、大双折射率差和低介电常数等特性,能够产生更快、更强的光学非线性响应,还具有优异的太赫兹波发射及吸收性能,是目前综合性能最优、应用最广、最具研究价值的非线性有机材料。近年光谱研究显示,DAST还表现出各向异性的太赫兹光谱特性。文章系统地总结了DAST晶体在太赫兹波发射、二次谐波产生、电光探测和电光调制等众多领域的国内外研究成果,还概括了我们团队近期在DAST基薄膜、太赫兹光谱、光电特性、超材料等新领域的研究进展。此外,还提出将准相位匹配法用于DAST晶体研究、利用电场或自组装膜诱导生长DAST晶体等新思路。对DAST成果的总结和梳理,将促进非线性DAST有机材料在电光调制器、频率转换器、THz探测器等重要领域的应用与理论研究,使其能够更加广泛地应用在光通信、光信息处理、军事技术等重要领域。     

From DNA to transistors

The rapid advance in molecular biology and nanotechnology opens up the possibility to explore the interface between biology and electronics at the single-molecule level. We focus on the organization of molecular electronic circuits. Interconnecting an immense number of molecular devices into a functional circuit and constructing a framework for integrated molecular electronics requires new concepts. A promising avenue relies on bottom-up assembly where the information for the circuit connectivity and functionality is embedded in the molecular building blocks. Biology can provide concepts and mechanisms for advancing this approach, but there is no straightforward way to apply them to electronics since biological molecules are essentially electrically insulating. Bridging the chasm between biology and electronics therefore presents great challenges. Circuit organization on the molecular scale is considered and contrasted with the levels of organization presented by the living world. The discussion then focuses on our proposal to harness DNA and molecular biology to construct the scaffold for integrated molecular electronics. DNA metallization is used to convert the DNA scaffold into a conductive one. We present the framework of sequence-specific molecular lithography based on the biological mechanism of homologous genetic recombination and carried out by the bacterial protein RecA. Molecular lithography enables us to use the information encoded in the scaffold DNA molecules for directing the construction of an electronic circuit. We show that it can lead all the way from DNA molecules to working transistors in a test-tube. Carbon nanotubes are incorporated as the active electronic components in the DNA-templated transistors. Our approach can, in principle, be applied to the fabrication of larger-scale electronic circuits. The realization of complex DNA-based circuits will, however, require new concepts and additional biological machinery allowing, for example, feedback from the electronic functionality to direct the assembly process and adaptation mechanisms.     

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.     

Neuronal-like Irregular Spiking Dynamics in Highly Volatile Memristive Intermediate-scale AgPt-Nanoparticle Assemblies

Neuromorphic computing seeks functional materials capable of emulating brain-like dynamics to solve computational problems with time and energy efficiency, outclassing current transistor-based hardware architectures. Major efforts are focused on integrating memristive devices into highly regular circuits (i.e., crossbar arrays), where the information representation in individual memristive devices is closely oriented toward the behavior of artificial neurons. However, artificial neurons are rather rigid mathematical concepts than realistic projections of complex neuronal dynamics. Neuroscience suggests that highly efficient information representation on the level of individual neurons relies on dynamical features such as excitatory and inhibitory contributions, irregularity of firing patterns, and temporal correlations. Here, a conductive atomic force microscopy approach is applied to probe the memristive dynamics of nanoscale assemblies of AgPt-nanoparticles at the stability border of the conducting state, where physical forces causing the formation and decay of filamentary structures appear to be balanced. This unveils a dynamic regime, where the memristive response is governed by irregular firing patterns. The significance of such a dynamical regime is motivated by close similarities to excitation and inhibition-governed behavior in biological neuronal systems, which is crucial to tune biological neuronal systems into a state most suitable for information representation and computation.     

磁性斯格明子的研究现状和展望

磁性斯格明子是具有拓扑保护性质的纳米尺度涡旋磁结构.斯格明子主要存在于非中心对称的手性磁性材料以及界面镜面对称性破缺的磁性薄膜材料中.因具有实空间的非平庸拓扑性,磁性斯格明子展现出丰富新奇的物理学特性,例如拓扑霍尔效应,新兴电磁动力学等,为研究拓扑自旋电子学提供了新的平台.另一方面,由于其具有尺寸小,高稳定性和易操控的特性,磁性斯格明子在未来高密度,低能耗,非易失性计算和存储器件中也具有潜在应用.现阶段的研究已经初步发现一系列磁斯格明子材料,并证明能够通过电流操控室温下稳定的磁性斯格明子,但是室温下单个斯格明子的精确产生、湮灭以及探测在实验上仍具有挑战性.本文阐述了磁性斯格明子的基础理论以及动力学研究现状,并对现有的斯格明子材料和斯格明子的产生,湮灭以及探测方法进行了总结,最后还对未来磁性斯格明子的物理理论研究以及应用发展中的挑战和机遇进行了讨论.     

A review of hyperspectral imaging for nanoscale materials research

As an emerging technology, hyperspectral imaging (HSI), which combines both advanced spectroscopy and imaging techniques, provides sufficient information for spectral and spatial analysis and is thus suitable for distribution and property investigation of nanoscale materials. Considering the applications of HSI have spread from remote sensing to quality control of macro products such as food and milk, this article reviews recent research of HSI in a new field of nanoscale materials. On the basis of fundamental parts of a HSI system, new techniques fitting specifically for nanoscale materials imaging such as dark field and Raman spectroscopy are introduced. Nanoscale materials, including metal nanoparticles, carbon nanotubes and graphene, biological components in cells and tissues, as well as multi-layer nanoscale materials, are the research hotspots utilizing HSI technology. Related research reports of the above materials are reviewed based on the physical distinction of these nanoscale materials. It is believed that HSI technology is a strongly potential technique for property investigation and manipulation of nanomaterial for various applications.     

二维有机拓扑绝缘体的研究进展

新材料的发现促进了科学与技术的进步.拓扑绝缘体是近期材料领域新的研究热点,相关研究的进一步深入,不仅加深了人们对材料物理性质的理解,也为其在自旋电子学和量子计算机等领域的潜在应用提供了有价值的参考.近年来,理论工作预测了一系列由金属和有机物构筑的二维有机拓扑绝缘体,本文主要介绍六角对称的金属有机晶格与Kagome金属有机晶格两类典型的二维有机拓扑绝缘体的研究进展,其中重点介绍了理论预测的氰基配位二维本征有机拓扑绝缘体.除了理论计算方面的工作,还简要介绍了关于二维有机拓扑绝缘体材料合成方面的实验工作.二维有机拓扑绝缘体的理论与实验研究不仅拓展了拓扑绝缘体的研究体系,还为寻找新的拓扑绝缘体材料提供了思路.     

Biologically-inspired stochastic vector matching for noise-robust information processing

“End of Moore’s Law” has recently become a topic. Keeping the signal-to-noise ratio (SNR) at the same level in the future will surely increase the energy density of smaller-sized transistors. Lowering the operating voltage will prevent this, but the SNR would inevitably degrade. Meanwhile, biological systems such as cells and brains possess robustness against noise in their information processing in spite of the strong influence of stochastic thermal noise. Inspired by the information processing of organisms, we propose a stochastic computing model to acquire information from noisy signals. Our model is based on vector matching, in which the similarities between the input vector carrying external noisy signals and the reference vectors prepared in advance as memorized templates are evaluated in a stochastic manner. This model exhibited robustness against the noise strength and its performance was improved by addition of noise with an appropriate strength, which is similar to a phenomenon observed in stochastic resonance. Because the stochastic vector matching we propose here has robustness against noise, it is a candidate for noisy information processing that is driven by stochastically-operating devices with low energy consumption in future. Moreover, the stochastic vector matching may be applied to memory-based information processing like that of the brain.     

以二乙烯硫/砜基为中心的新型电荷转移分子双光子吸收特性

理论研究分子结构与双光子吸收性质之间的关系对于指导实验者设计与合成功能分子材料具有重要意义. 在杂化密度泛函水平上, 利用响应函数方法, 计算了一类以二乙烯硫/砜基为中心的新型电荷转移分子的双光子吸收截面, 并在相同计算水平上, 与联苯乙烯类强双光子吸收分子做了比较; 以新型电荷转移分子为基础, 利用异构效应, 设计出了可以增强双光子吸收强度的分子结构. 研究表明, 在可应用波长范围内, 该系列分子表现出较强的双光子吸收响应, 与相似共轭长度的强双光子吸收分子具有相同量级的双光子吸收截面; 二乙烯硫/砜基在分子中心作为吸电子基团可以形成有效的电荷转移分子; 改变咔唑基的连接方式可以有效提高双光子吸收截面. 该研究为实验合成新型双光子吸收分子材料提供了理论依据.     

Classical Concepts in Quantum Programming

The rapid progress of computer technology has been accompanied by a corresponding evolution of software development, from hardwired components and binary machine code to high level programming languages, which allowed to master the increasing hardware complexity and fully exploit its potential. This paper investigates, how classical concepts like hardware abstraction, hierarchical programs, data types, memory management, flow of control, and structured programming can be used in quantum computing. The experimental language QCL will be introduced as an example, how elements like irreversible functions, local variables, and conditional branching, which have no direct quantum counterparts, can be implemented, and how nonclassical features like the reversibility of unitary transformation or the nonobservability of quantum states can be accounted for within the framework of a procedural programming language.