An Introduction to Quantum Computing for Statisticians and Data Scientists
It provides an accessible introduction for statisticians and data scientists to understand and collaborate in quantum computing, which is incremental as it reviews existing concepts without presenting new research.
This review introduces quantum computing to statisticians and data scientists, explaining its potential to solve critical problems like pharmaceutical design and machine learning optimization, and aims to equip them with the knowledge to engage with quantum literature and develop new tools.
Quantum computers promise to surpass the most powerful classical supercomputers when it comes to solving many critically important practical problems, such as pharmaceutical and fertilizer design, supply chain and traffic optimization, or optimization for machine learning tasks. Because quantum computers function fundamentally differently from classical computers, the emergence of quantum computing technology will lead to a new evolutionary branch of statistical and data analytics methodologies. This review provides an introduction to quantum computing designed to be accessible to statisticians and data scientists, aiming to equip them with an overarching framework of quantum computing, the basic language and building blocks of quantum algorithms, and an overview of existing quantum applications in statistics and data analysis. Our goal is to enable statisticians and data scientists to follow quantum computing literature relevant to their fields, to collaborate with quantum algorithm designers, and, ultimately, to bring forth the next generation of statistical and data analytics tools.