Quantum walks, the quantum mechanical counterpart of classical random walks, is an advanced tool for building quantum algorithms that has been recently shown to constitute a universal model of quantum computation. Quantum walks is now a solid field of research of quantum computation full of exciting open problems for physicists, computer scientists and engineers. In this paper we review theoretical advances on the foundations of both discrete and continuous-time quantum walks, together with the role that randomness plays in quantum walks, the connections between the mathematical models of coined discrete quantum walks and continuous quantum walks, the quantumness of quantum walks, a summary of papers published on discrete quantum walks and entanglement as well as a succinct review of experimental proposals and realizations of discrete-time quantum walks. Furthermore, we have reviewed several algorithms based on both discrete- and continuous-time quantum walks as well as a most important result: the computational universality of both continuous- and discrete-time quantum walks.

Basic_Complex_Analysis__A_Comprehensive_Course_in_Analysis，_Part_2B

Basic_Complex_Analysis__A_Comprehensive_Course_in_Analysis，_Part_2A——Simon

2015_Harmonic_Analysis__A_Comprehensive_Course_in_Analysis，_Part_3

The Comprehensive LATEX Symbol List-symbols-a4

Comprehensive Handbook of Chemical Bond Energies 0849373662

【伯克利干货】机器学习全面指南 CS189是加州大学伯克利分校的机器学习课程。在本指南中，我们创建了一个全面的课程指南，以便与学生和公众分享我们的知识，并希望吸引其他大学的学生对伯克利的机器学习课程的兴趣。本指南由cs189助教Soroush Nasiriany和Garrett Thomas于2017年秋季发起，并得到William Wang和Alex Yang的协助。

The CUDA Handbook begins where CUDA by Example (Addison-Wesley, 2011) leaves off, discussing CUDA hardware and software in greater detail and covering both CUDA 5.0 and Kepler. Every CUDA developer, from the casual to the most sophisticated, will find something here of interest and immediate usefulness. Newer CUDA developers will see how the hardware processes commands and how the driver checks progress; more experienced CUDA developers will appreciate the expert coverage of topics such as the driver API and context migration, as well as the guidance on how best to structure CPU/GPU data interchange and synchronization. The accompanying open source code-more than 25,000 lines of it, freely available at www.cudahandbook.com-is specifically intended to be reused and repurposed by developers. Designed to be both a comprehensive reference and a practical cookbook, the text is divided into the following three parts: Part I, Overview, gives high-level descriptions of the hardware and software that make CUDA possible. Part II, Details, provides thorough descriptions of every aspect of CUDA, including * Memory * Streams and events * Models of execution, including the dynamic parallelism feature, new with CUDA 5.0 and SM 3.5 * The streaming multiprocessors, including descriptions of all features through SM 3.5 * Programming multiple GPUs * Texturing The source code accompanying Part II is presented as reusable microbenchmarks and microdemos, designed to expose specific hardware characteristics or highlight specific use cases. Part III, Select Applications, details specific families of CUDA applications and key parallel algorithms, including * Streaming workloads * Reduction * Parallel prefix sum (Scan) * N-body * Image ProcessingThese algorithms cover the full range of potential CUDA applications.

A Comprehensive Guide to Machine Learning

元分析软件，快速进行数据整合分析。元分析(meta-analysis )统计方法是对众多现有实证文献的再次统计,通过对相关文献中的统计指标利用相应的统计公式,进行再一次的统计分析,从而可以根据获得的统计显著性等来分析两个变量间真实的相关关系。