Quantum gradient estimation

Cambridge University Press eBooks, pp. 276–280

Abstract

This chapter covers the quantum algorithmic primitive called quantum gradient estimation, where the goal is to output an estimate for the gradient of a multivariate function. This primitive features in other primitives, for example, quantum tomography. It also features in several quantum algorithms for end-to-end problems in continuous optimization, finance, and machine learning, among other areas. The size of the speedup it provides depends on how the algorithm can access the function, and how difficult the gradient is to estimate classically.

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