Quantum interior point methods

Cambridge University Press eBooks, pp. 291–298

Abstract

This chapter covers quantum interior point methods, which are quantum algorithmic primitives for application to convex optimization problems, particularly linear, second-order, and semidefinite programs. Interior point methods are a successful classical iterative technique that solve a linear system of equations at each iteration. Quantum interior point methods replace this step with quantum a quantum linear system solver combined with quantum tomography, potentially offering a polynomial speedup.

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