Trade-offs between Error Exponents and Excess-Rate Exponents of Typical Slepian–Wolf Codes

Typical random codes (TRCs) in a communication scenario of source coding with side information in the decoder is the main subject of this work. We study the semi-deterministic code ensemble, which is a certain variant of the ordinary random binning code ensemble. In this code ensemble, the relatively small type classes of the source are deterministically partitioned into the available bins in a one-to-one manner. As a consequence, the error probability decreases dramatically. The random binning error exponent and the error exponent of the TRCs are derived and proved to be equal to one another in a few important special cases. We show that the performance under optimal decoding can be attained also by certain universal decoders, e.g., the stochastic likelihood decoder with an empirical entropy metric. Moreover, we discuss the trade-offs between the error exponent and the excess-rate exponent for the typical random semi-deterministic code and characterize its optimal rate function. We show that for any pair of correlated information sources, both error and excess-rate probabilities exponential vanish when the blocklength tends to infinity.     

Discovering Higher-Order Interactions Through Neural Information Decomposition

If regularity in data takes the form of higher-order functions among groups of variables, models which are biased towards lower-order functions may easily mistake the data for noise. To distinguish whether this is the case, one must be able to quantify the contribution of different orders of dependence to the total information. Recent work in information theory attempts to do this through measures of multivariate mutual information (MMI) and information decomposition (ID). Despite substantial theoretical progress, practical issues related to tractability and learnability of higher-order functions are still largely unaddressed. In this work, we introduce a new approach to information decomposition—termed Neural Information Decomposition (NID)—which is both theoretically grounded, and can be efficiently estimated in practice using neural networks. We show on synthetic data that NID can learn to distinguish higher-order functions from noise, while many unsupervised probability models cannot. Additionally, we demonstrate the usefulness of this framework as a tool for exploring biological and artificial neural networks.     

基于五离子最大纠缠态的优化隐形传态和超密集编码

我们提出了一个制备五离子最大纠缠态的方案,并利用该纠缠态实现任意两量子比特的隐形传送和超密集编码。超密集编码的信息容量满足Holevo bound,这意味着通过发送三个量子比特可以传送五个经典比特的信息。     

Classification of quantum phases and topology of logical operators in an exactly solved model of quantum codes

Searches for possible new quantum phases and classifications of quantum phases have been central problems in physics. Yet, they are indeed challenging problems due to the computational difficulties in analyzing quantum many-body systems and the lack of a general framework for classifications. While frustration-free Hamiltonians, which appear as fixed point Hamiltonians of renormalization group transformations, may serve as representatives of quantum phases, it is still difficult to analyze and classify quantum phases of arbitrary frustration-free Hamiltonians exhaustively. Here, we address these problems by sharpening our considerations to a certain subclass of frustration-free Hamiltonians, called stabilizer Hamiltonians, which have been actively studied in quantum information science. We propose a model of frustration-free Hamiltonians which covers a large class of physically realistic stabilizer Hamiltonians, constrained to only three physical conditions; the locality of interaction terms, translation symmetries and scale symmetries, meaning that the number of ground states does not grow with the system size. We show that quantum phases arising in two-dimensional models can be classified exactly through certain quantum coding theoretical operators, called logical operators, by proving that two models with topologically distinct shapes of logical operators are always separated by quantum phase transitions.     

Deterministic quantum dense coding with cluster state in optical systems

We propose two optical schemes for implementing the deterministic single-particle and two-particle quantum dense coding using four-qubit cluster states. In the protocols, the photon is neuter particle, so it has longer decoherence time with the environment than other particles. It is easy to implement single-bit gate using the linear optical elements under certain conditions, so the transformations performed on the photons by Alice can be easily achieved. Here the cluster states can be exactly discriminated using the parity detector, PBS and FS-PBS. In addition, the success probabilities of the dense coding are both equal to 1.     

绝对编码光栅的相位细分及其在位移测量中的应用

提出通过光栅条纹相位的精密测量,获取光栅高精度位移信息的方法。具体方法是对光栅图像采用多码道设计,用CCD二维图像传感器获取测量段光栅图像多码道信息。对最低码道图形的周期函数序列进行傅里叶变换、基频滤波和逆傅里叶变换获得光栅截断相位分布,其余码道信息提供相位展开的级次,以此获得测量段光栅的绝对相位分布。用光刻的手段制作了实用的绝对编码光栅,基元码道的尺寸是:27.36μm用于明条纹,27.36μm用于暗条纹,最小基元码道空间周期为54.72μm,光栅长度为14008.32μm。在步长近似3μm的位移测试中,与比对的标准仪器记录值比较,标准偏差为0.2057μm,精度在亚微米量级。重复性实验表明,位置测试的稳定性为0.09μm(标准差),得到600倍以上细分的分辨力。     

一种基于极坐标编码的果蝇优化算法

针对果蝇优化算法易陷入早熟收敛、收敛速度慢、寻优精度低的缺点,提出一种基于极坐标编码的果蝇优化算法.为提高果蝇优化算法的寻优精度,采用极坐标编码的形式,以增加单个母体寻优空间表示方法的多样性,并使种群中的个体,在围绕个体的整个超球体内随机搜索,使个体的搜索范围更加广泛.在迭代寻优过程中,根据适应度值和概率调整极角,逐渐降低观测结果的不确定性.通过9个基准测试函数,对基于极坐标编码的果蝇优化算法进行仿真实验,结果表明了算法在收敛性和稳定性方面,优于其它5个优化算法,测试结果验证了极坐标编码方法的有效性和可行性.     

量子通信

量子通信是经典通信和量子力学相结合的一门新兴交叉学科。文章综述了量子通信领域的研究进展，既包括人们所熟知的量子隐形传态、密集编码和量子密码学，也包括刚刚兴起但却有巨大潜力的量子通信复杂度和远程量子通信等领域。文章介绍了量子通信的基本理论框架，同时也涉及了这个领域最新的实验研究的进展。