Quantum algorithms for scientific computing

TL;DR

This review discusses how quantum computing could revolutionize scientific computing by enabling advanced simulations, optimization, and machine learning, highlighting current challenges and potential impacts across various scientific fields.

Contribution

It provides a comprehensive overview of the scientific and technological advances needed for quantum advantage in scientific computing applications.

Findings

Quantum algorithms could significantly improve simulation and optimization tasks.

Challenges include data encoding and hardware speed mismatches.

Potential for transformative impacts in weather, aerospace, and sustainable materials.

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, 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.