Implementing Classical Hadamard Transform Algorithm by Continuous Variable Cluster State

Measurement-based one-way quantum computation, which uses cluster states as resources, provides an efficient model to perforrn computation. However, few of the continuous variable （CV） quantum algorithms and classical algorithms based on one-way quantum computation were proposed. In this work, we propose a method to implement the classical Hadamard transform algorithm utilizing the CV cluster state. Compared with classical computation, only half operations are required when it is operated in the one-way CV quantum computer. As an example, we present a concrete scheme of four-mode classical Hadamard transform algorithm with a four-partite CV cluster state. This method connects the quantum computer and the classical algorithms, which shows the feasibility of running classical algorithms in a quantum computer efficiently.

Cavity control as a new quantum algorithms implementation treatment

Based on recent experiments [Nature 449, 438 （2007） and Nature Physics 6, 777 （2010）], a new approach for realizing quantum gates for the design of quantum algorithms was developed. Accordingly, the operation times of such gates while functioning in algorithm applications depend on the number of photons present in their resonant cavities. Multi-qubit algorithms can be realized in systems in which the photon number is increased slightly over the qubit number. In addition, the time required for operation is considerably less than the dephasing and relaxation times of the systems. The contextual use of the photon number as a main control in the realization of any algorithm was demonstrated. The results indicate the possibility of a full integration into the realization of multi-qubit multiphoton states and its application in algorithm designs. Yhrthermore, this approach will lead to a successful implementation of these designs in future experiments.

Research and Implementation of the Practical Texture Synthesis Algorithms

How to generate pictures real and esthetic objects is an important subject of computer graphics. The techniques of mapping textures onto the surfaces of an object in the 3D space are efficient ap- proaches for the purpose.We developed and implemented algorithms for generating objects with appear ances stone,wood grain,ice lattice,brick,doors and windows on Apollo workstations. All the algorithms have been incorporated into the 3D geometry modelling system(GEMS)developed by the CAD Center of Tsinghua University.This paper emphasizes the wood grain and the ice lattice algorithms.

Analysis and implementation of theFMLMS algorithm in AANC

Although the Filtered-X LMS (FLMS) algorithm is widely used in the Adaptive Active Noise Control (AANC) system due to its simplicity, its applications to an AANC are limited because it is intensively sensitive to the dispersion of the eigenvalues of the autocorrelation matrix of the input signal vector. The Filtered-X Momentum LMS (FMLMS) algorithm proposed in this paper includes a momentum term to weight update equation of the FLMS algorithm. From the theoretical analysis, the computer simulations and the experimental resultsin a half-anechoic room, it is shown that the FMLMS algorithm has more advantages than the FLMS algorithm for a single-path AANC system.

EvolveKG:a general framework to learn evolving knowledge graphs

A great many practical applications have observed knowledge evolution,i.e.,continuous born of new knowledge,with its formation influenced by the structure of historical knowledge.This observation gives rise to evolving knowledge graphs whose structure temporally grows over time.However,both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored.To this end,we propose EvolveKG–a general framework that enables algorithms in the static knowledge graphs to learn the evolving ones.EvolveKG quantifies the influence of a historical fact on a current one,called the effectiveness of the fact,and makes knowledge prediction by leveraging all the cross-time knowledge interaction.The novelty of EvolveKG lies in Derivative Graph–a weighted snapshot of evolution at a certain time.Particularly,each weight quantifies knowledge effectiveness through a temporarily decaying function of consistency and attenuation,two proposed factors depicting whether or not the effectiveness of a fact fades away with time.Besides,considering both knowledge creation and loss,we obtain higher prediction accuracy when the effectiveness of all the facts increases with time or remains unchanged.Under four real datasets,the superiority of EvolveKG is confirmed in prediction accuracy.

Near real-time gravitational wave data analysis of the massive black hole binary with TianQin

Space-borne gravitational wave(GW)detectors can detect the merger of massive black holes.The early warning and localization of GW events before merging can be used to inform electromagnetic telescopes and conduct multimessenger observations.However,this requires real-time data transmission and analysis capabilities.The geocentric orbit of the space-borne GW detector Tian Qin makes it possible to conduct real-time data transmission.In this study,we develop a search and localization pipeline for massive black hole binaries(MBHBs)with Tian Qin under both regular and real-time data transmission modes.We demonstrate that,with real-time data transmission,MBHBs can be accurately localized on the fly.With the approaching merger,each analysis can be finished in only 40 min.For an MBHB system at a distance of 1 Gpc,if we receive data every hour,then we can pinpoint its location to within less than 1 deg^(2)on the final day before the merger.