摘要
大数据技术的迅猛发展对计算效率提出了更高的要求.由于量子系统的独特性质,量子计算具有经典计算不具有的量子超并行计算能力,能够对某些重要的经典算法进行加速.人们发现,除了大数分解算法,量子计算的更多用途是对量子体系的仿真计算和在数据分析领域的应用.近年来,大数据和量子计算开始融合.虽然实际使用的量子计算机尚未建成,量子计算在大数据的应用在理论上已经取得了一些重要的进展.实验上也有了一些发展.本文首先介绍量子计算的基本原理和Grover量子算法.随后以量子机器学习作为切入点,介绍了量子计算在数据挖掘领域的应用.
With the explosion of big data, higher requirements for computational efficiency have emerged. Compared with classical computing, quantum computing possesses quantum parallelism due to the unique nature of quantum systems. It has been found that many classical algorithms can be accelerated using quantum computing. In addition to factorizing a large integer, quantum computers can be used for data processing and analysis. In recent years, two frontiers, i.e., big data and quantum computing have begun to merge. Though practical quantum computers have not yet been built, theoretical studies have made some important progress. In this review, we introduce the basic principles of quantum computing. As a representative example, we describe the Grover search algorithm and its important generalizations. Quantum machine learning is the entry point for the integration of big data with quantum computation. We review in detail, the applications of quantum computation in data mining, the main application of machine learning. Other aspects of quantum computing in big data are also briefly summarized.
出处
《科学通报》
EI
CAS
CSCD
北大核心
2015年第5期499-508,共10页
Chinese Science Bulletin
基金
国家自然科学基金(91221205,11175094)
国家重点基础研究发展计划(2011CB9216002)资助
关键词
大数据
量子计算
量子机器学习
量子信息处理
big data, quantum computation, quantum machine learning, quantum information processing
