An introduction to quantum computing for statisticians and data scientists

Anna Lopatnikova; Minh-Ngoc Tran; Scott A. Sisson

doi:10.3934/fods.2024013

Article Contents

2024, Volume 6, Issue 3: 278-307. Doi: 10.3934/fods.2024013

This issue Previous Article Sparse joint shift in multinomial classification Next Article Convergence analysis of the rank-restricted soft SVD algorithm

An introduction to quantum computing for statisticians and data scientists

1.
Discipline of Business Analytics, the University of Sydney, Australia
2.
UNSW Data Science Hub and School of Mathematics and Statistics, University of New South Wales, Australia

^*Corresponding author: Minh-Ngoc Tran
^*Corresponding author: Minh-Ngoc Tran

Received: August 2023

Revised: March 2024

Early access: April 2024

Published online: April 2024

This work was supported by the Australian Research Council (ARC) under Grant DP200103015 and Grant FT170100079; ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) under Grant CE140100049; ARC Training Centre in Data Analytics for Resources and Environments under Grant IC190100031.

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Abstract

Quantum computers promise to surpass the most powerful classical supercomputers in tackling critical practical problems, such as designing pharmaceuticals and fertilizers, optimizing supply chains and traffic, and enhancing machine learning. Since quantum computers operate fundamentally differently from classical ones, their emergence will give rise to a new evolutionary branch of statistical and data analytics methodologies. This review aims to provide an introduction to quantum computing accessible to statisticians and data scientists, equipping them with a comprehensive framework, the basic language, and building blocks of quantum algorithms, as well as an overview of existing quantum applications in statistics and data analysis. Our objective is to empower statisticians and data scientists to follow quantum computing literature relevant to their fields, collaborate with quantum algorithm designers, and ultimately drive the development of the next generation of statistical and data analytics tools.

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Mathematics Subject Classification: Primary: 81P68, 62A01; Secondary: 81-01.

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