Solving differential equations

Cambridge University Press eBooks, pp. 111–129

Abstract

This chapter covers quantum algorithms for numerically solving differential equations and the areas of application where such capabilities might be useful, such as computational fluid dynamics, semiconductor chip design, and many engineering workflows. We focus mainly on algorithms for linear differential equations (covering both partial and ordinary linear differential equations), but we also mention the additional nuances that arise for nonlinear differential equations. We discuss important caveats related to both the data input and output aspects of an end-to-end differential equation solver, and we place these quantum methods in the context of existing classical methods currently in use for these problems.

Authors 4

1. Sam McArdle
   Affiliation as printed

   AWS Center for Quantum Computing

2. Connor T. Hann
   Affiliation as printed

   AWS Center for Quantum Computing

3. Aleksander Kubica

   Yale University

   Affiliation as printed

   Yale University

4. Fernando G. S. L. Brandão
   Affiliation as printed

   AWS Center for Quantum Computing