Basic linear algebra methods for quantum problems

TL;DR

This paper reviews fundamental linear algebra routines essential for quantum problem-solving, emphasizing their implementation, computational complexity, and relevance to quantum eigenvalue problems.

Contribution

It provides a comprehensive overview of basic linear algebra operations and their efficient computational implementations for quantum applications.

Findings

Covers solutions to eigenvalue problems in quantum systems.

Discusses algorithms for matrix decompositions used in modern libraries.

Highlights the importance of optimized routines for quantum computations.

Abstract

Making new methods for quantum problems often relies on using basic operations in linear algebra. Often these routines are hidden behind well-known libraries that have been optimized over decades. Attempting to improve on those basic routines would be highly time-consuming. We aim in this article to review those basic routines and provide a knowledge foundation for how to perform basic operations on a computer that would be inaccessible with pen and paper. Elementary details on the solutions to linear algebra problems and computational complexity are reviewed. The focus is on solving eigenvalue problems for quantum systems, but the discussion is generic to many other applications. Common matrix forms relevant to quantum systems and their solution strategies are covered. The discussion extends to computational numerical methods for which the most efficient functions exist in freely available libraries. These include eigenvalue, Schur, QR, LU, LDL, Cholesky, and singular value decompositions. The algorithms for obtaining some of these decompositions are discussed, with focus being placed on those used in modern libraries.