Quantum algorithms for scientific computing.

Au-Yeung, Rhonda; Camino, Bruno; Rathore, Omer; Kendon, Viv

Abstract

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, engineering, aerospace, drug design, and the design of ``green'' materials for sustainable development. This requires significant effort from the computational science, engineering and quantum computing communities working together.
. [Abstract copyright: Creative Commons Attribution license.]

Citation

Au-Yeung, R., Camino, B., Rathore, O., & Kendon, V. (2024). Quantum algorithms for scientific computing. Reports on Progress in Physics, 87(11), Article 116001. https://doi.org/10.1088/1361-6633/ad85f0

• Journal Article Type: Article
• Acceptance Date: Oct 11, 2024
• Online Publication Date: Oct 29, 2024
• Publication Date: 2024-11
• Deposit Date: Nov 1, 2024
• Publicly Available Date: Nov 1, 2024
• Journal: Reports on Progress in Physics
• Print ISSN: 0034-4885
• Electronic ISSN: 1361-6633
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 87
• Issue: 11
• Article Number: 116001
• DOI: https://doi.org/10.1088/1361-6633/ad85f0
• Keywords: quantum computing, quantum algorithms, scientific computing
• Public URL: https://durham-repository.worktribe.com/output/2988924