Scalable Quantum Computational Science: A Perspective from Block-Encodings and Polynomial Transformations

TL;DR

This paper discusses how block-encodings and polynomial transformations can create scalable quantum algorithms, bridging the gap between theoretical quantum computing and practical scientific applications in chemistry, physics, and optimization.

Contribution

It introduces a unified framework using block-encodings and polynomial transformations, highlighting recent advancements and potential for scalable quantum computational science.

Findings

Construction and assembly of block-encodings

Generalizations of quantum signal processing algorithms

Applications in chemistry, physics, and optimization

Abstract

Significant developments made in quantum hardware and error correction recently have been driving quantum computing towards practical utility. However, gaps remain between abstract quantum algorithmic development and practical applications in computational sciences. In this Perspective article, we propose several properties that scalable quantum computational science methods should possess. We further discuss how block-encodings and polynomial transformations can potentially serve as a unified framework with the desired properties. Recent advancements on these topics are presented including construction and assembly of block-encodings, and various generalizations of quantum signal processing (QSP) algorithms to perform polynomial transformations. The scalability of QSP methods on parallel and distributed quantum architectures is also highlighted. Promising applications in simulation and observable estimation in chemistry, physics, and optimization problems are presented. We hope this Perspective serves as a gentle introduction of state-of-the-art quantum algorithms to the computational science community, and inspires future development on scalable quantum computational science methodologies that bridge theory and practice.