Abstract
Quantum computation and quantum information have attracted considerable attention on multiple frontiers of scientific fields ranging from physics to chemistry and engineering, as well as from computer science to mathematics and statistics. Data science combines statistical methods, computational algorithms, and domain science information to extract knowledge and insights from big data, and to solve complex real-world problems. While it is well-known that quantum computation has the potential to revolutionize data science, much less has been said about the potential of data science to advance quantum computation. Yet because the stochasticity of quantum physics renders quantum computation random, data science can play an important role in the development of quantum computation and quantum information. This article gives an overview of quantum computation and promotes interplay between quantum science and data science. Overall, it advocates for the development of quantum data science for advancing quantum computation and quantum information.
Highlights
The interface of statistics and computation is a signature issue in data science, which characteristically uses statistics, computation, and domain science knowledge to extract information and insights from data for the solving of big data problems
Quantum computation and quantum information have attracted considerable attention on multiple frontiers of scientific fields ranging from physics to chemistry and engineering, as well as from computer science to mathematics and statistics
Data science combines statistical methods, computational algorithms, and domain science information to extract knowledge and insights from big data, and to solve complex real-world problems. While it is well-known that quantum computation has the potential to revolutionize data science, much less has been said about the potential of data science to advance quantum computation
Summary
The interface of statistics and computation is a signature issue in data science, which characteristically uses statistics, computation, and domain science knowledge to extract information and insights from data for the solving of big data problems. The current computing demand in machine learning pushes the limits of computer technology; as such, the interplay between data science (machine learning) and computation (computer hardware) becomes more important than ever. More powerful hardware can help in developing data science methods to scale to the enormous size of big data, and data science may affect computer chip fabrication technology for designing sophisticated chips for carrying out machine learning tasks. In turn, data science, especially in the form of machine learning, can play an important role in the development of quantum computation and quantum information. Quantum computing scientists who would like to understand the role of statistics and data science in quantum computation may jump from Section 1 to Section 5.2.2 and Section 7. Readers who would like to quickly learn something about quantum computational complexity and its impact on physics and pure mathematics may directly go from Sections 2 and 3 to Sections 5.1 and 6
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