An Introduction to Quantum Computing for Statisticians and Data Scientists

Abstract

Quantum computing has the potential to revolutionise and change the way we live and understand the world. This review aims to provide an accessible introduction to quantum computing with a focus on applications in statistics and data analysis. We start with an introduction to the basic concepts necessary to understand quantum computing and the differences between quantum and classical computing. We describe the core quantum subroutines that serve as the building blocks of quantum algorithms. We then review a range of quantum algorithms expected to deliver a computational advantage in statistics and machine learning. We highlight the challenges and opportunities in applying quantum computing to problems in statistics and discuss potential future research directions.

An introduction to quantum computing for statisticians and data scientists

“…Given the inherent noise in current quantum hardware, statistical approaches are particularly well-suited for finding optimal parameters, as they can effectively account for and mitigate the effects of noise. More detailed discussion can be found in Section 10 of the extended version [102].…”

“…Section 6 concludes. An extended version of this review, [102], discusses quantum algorithms and their building blocks in greater detail.…”