大数据环境下量子机器学习的研究进展及发展趋势

复杂性是大数据区别于传统数据的根本所在，大数据的复杂性必然带来不确定性，如何高效、安全、准确地处理大数据所具有的复杂性和不确定性问题已经成为实现大数据知识发现的前提和关键。该文分析了目前大数据环境下不确定性集合理论和大数据计算与分析方法、机器学习、量子计算及量子机器学习的研究现状和不足，展望了未来的发展趋势，指出在即将来临的“大数据+人工智能+量子计算”时代，将“大数据+不确定性集合理论+机器学习+量子计算”交叉融合研究既有理论和现实意义，又有实用价值，也必将成为智慧化时代大数据领域的研究热点。

Research Progress and Perspectives of Quantum Machine Learning in Big Data Environment

Complexity is what makes big data and traditional data fundamentally different. The complexity of big data inevitably brings uncertainty. How to deal with the complexity and uncertainty of big data efficiently, safely and accurately has become the premise and key to big data exploration. This paper analyzes the shortcomings of the uncertainty set theory, big data computing and analytics, machine learning, quantum computing and quantum machine learning in big data environment in-depth, looks forward to the future development trends and points out that the cross fusion of "big data + uncertainty set theory + machine learning algorithm + quantum computing " in the coming era of "big data + artificial intelligence + quantum computing" is of value in both theoretical and actual significance, also will become a research hotspot in the field of intelligent era of big data.