Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2212.02133 · Data-Centric Engineering

Quantum computing for data-centric engineering and science

Steven Herbert

Published Jan 1, 2022• 5 citations
arXivHTMLPDF

Abstract

Abstract In this perspective, I give my answer to the question of how quantum computing will impact on data-intensive applications in engineering and science. I focus on quantum Monte Carlo integration as a likely source of (relatively) near-term quantum advantage, but also discuss some other ideas that have garnered widespread interest.
DOI
10.1017/dce.2022.36

Related papers

Quantum Computing Applications in Engineering: A Comprehensive Review of Algorithms, Hardware, and Future Prospects

Unni Krishnan

Quantum computing has emerged as a transformative technology with the potential to revolutionize various fields, including engineering. This paper provides a comprehensive review of the applications of quantum computing in engineering, focusing on algorithms, hardware, and future prospects. We explore the fundamental principles of quantum computing, the current state of quantum hardware, and the key algorithms that have been developed for engineering problems. We also discuss the challenges and opportunities in integrating quantum computing into engineering workflows and provide a forward-looking perspective on the future of this field. This review aims to serve as a valuable resource for researchers, engineers, and practitioners interested in the intersection of quantum computing and engineering

International Journal of Artificial Intelligence Data Science and Machine Learning•Jan 1, 2024•0 citations

Quantum algorithms for scientific computing

Rhonda Au-Yeung, Bruno Camino, Omer Rathore +1 more

Quantum computing promises to provide the next step up in computational power for diverse application areas. In this review, we examine the science behind the quantum hype, and the breakthroughs required to achieve true quantum advantage in real world applications. Areas that are likely to have the greatest impact on high performance computing (HPC) include simulation of quantum systems, optimization, and machine learning. We draw our examples from electronic structure calculations and computational fluid dynamics which account for a large fraction of current scientific and engineering use of HPC. Potential challenges include encoding and decoding classical data for quantum devices, and mismatched clock speeds between classical and quantum processors. Even a modest quantum enhancement to current classical techniques would have far-reaching impacts in areas such as weather forecasting, aerospace engineering, and the design of 'green' materials for sustainable development. This requires significant effort from the computational science, engineering and quantum computing communities working together.

arXiv:2312.14904•Reports on Progress in Physics•Oct 11, 2024•20 citations

Quantum Optimization in Engineering: A Brief Review

Yousra Mahmoudi, Nadjet Zioui, Hacène Belbachir

Since its beginning, quantum computing has opened up a plethora of new perspectives and advantages., including its ability to exponentially accelerate computing processes. A large amount of research has been devoted to developing innovative methods for solving a variety of engineering problems by designing quantum versions of numerous mathematical processes. In this paper, we review the state of the art in these new techniques from the standpoint of quantum computer creation and efficiency enhancement, with an emphasis on the most widespread mathematical optimization techniques for engineering applications. It also emphasizes the obstacles and constraints associated with the use of quantum computing, as well as the major prospects for future contributions. This paper is providing a useful resource for engineering researchers who are likely to seek solutions from quantum computing.

International Conference on Decision Aid Sciences and Applications (DASA)•Sep 16, 2023•3 citations

Data-Centric Quantum Machine Learning: Data-to-Models Continuum

Marcelo Rita Pias, Everson da Silva Flores, Bruna de Vargas Guterres +2 more

This topical review examines a data-centric perspective in quantum machine learning (QML). Existing work has focused on circuits and model design. Data preparation and representation have received less attention. This paper addresses two questions. First, what forms of data processing are required across representative QML methods. Second, how data-centric practices affect accuracy and scalability. A taxonomy classifies QML architectures according to data-handling requirements. A structured data-to-model workflow is discussed that comprises data ingestion, abstraction, tokenization, encoding and synthetic data generation. Results from recent work indicate that data preparation likely affects performance in near-term quantum systems.

IEEE Access•Jan 1, 2026•0 citations

Quantum computing for data science

Barry C. Sanders

Abstract I provide a perspective on the development of quantum computing for data science, including a dive into state-of-the-art for both hardware and algorithms and the potential for quantum machine learning.

arXiv:2302.08666•Journal of Physics Conference Series•Feb 1, 2023•5 citations

Quantum Computing

James Frederick Amundson, E. Sexton-Kennedy

In recent years Quantum Computing has attracted a great deal of attention in the scientific and technical communities. Interest in the field has expanded to include the popular press and various funding agencies. We discuss the origins of the idea of using quantum systems for computing. We then give an overview in recent developments in quantum hardware and software, as well as some potential applications for high energy physics.

EPJ Web of Conferences•Jan 1, 2019•5 citations
Quantum computing has emerged as a transformative technology with the potential to revolutionize various fields, including engineering. This paper provides a comprehensive review of the applications of quantum computing in engineering, focusing on algorithms, hardware, and future prospects. We explore the fundamental principles of quantum computing, the current state of quantum hardware, and the key algorithms that have been developed for engineering problems. We also discuss the challenges and opportunities in integrating quantum computing into engineering workflows and provide a forward-looking perspective on the future of this field. This review aims to serve as a valuable resource for researchers, engineers, and practitioners interested in the intersection of quantum computing and engineering
Quantum computing promises to provide the next step up in computational power for diverse application areas. In this review, we examine the science behind the quantum hype, and the breakthroughs required to achieve true quantum advantage in real world applications. Areas that are likely to have the greatest impact on high performance computing (HPC) include simulation of quantum systems, optimization, and machine learning. We draw our examples from electronic structure calculations and computational fluid dynamics which account for a large fraction of current scientific and engineering use of HPC. Potential challenges include encoding and decoding classical data for quantum devices, and mismatched clock speeds between classical and quantum processors. Even a modest quantum enhancement to current classical techniques would have far-reaching impacts in areas such as weather forecasting, aerospace engineering, and the design of 'green' materials for sustainable development. This requires significant effort from the computational science, engineering and quantum computing communities working together.
Since its beginning, quantum computing has opened up a plethora of new perspectives and advantages., including its ability to exponentially accelerate computing processes. A large amount of research has been devoted to developing innovative methods for solving a variety of engineering problems by designing quantum versions of numerous mathematical processes. In this paper, we review the state of the art in these new techniques from the standpoint of quantum computer creation and efficiency enhancement, with an emphasis on the most widespread mathematical optimization techniques for engineering applications. It also emphasizes the obstacles and constraints associated with the use of quantum computing, as well as the major prospects for future contributions. This paper is providing a useful resource for engineering researchers who are likely to seek solutions from quantum computing.
This topical review examines a data-centric perspective in quantum machine learning (QML). Existing work has focused on circuits and model design. Data preparation and representation have received less attention. This paper addresses two questions. First, what forms of data processing are required across representative QML methods. Second, how data-centric practices affect accuracy and scalability. A taxonomy classifies QML architectures according to data-handling requirements. A structured data-to-model workflow is discussed that comprises data ingestion, abstraction, tokenization, encoding and synthetic data generation. Results from recent work indicate that data preparation likely affects performance in near-term quantum systems.