A Survey of Quantum Learning Theory
Quantum Physics
2017-07-31 v3 Computational Complexity
Machine Learning
Abstract
This paper surveys quantum learning theory: the theoretical aspects of machine learning using quantum computers. We describe the main results known for three models of learning: exact learning from membership queries, and Probably Approximately Correct (PAC) and agnostic learning from classical or quantum examples.
Cite
@article{arxiv.1701.06806,
title = {A Survey of Quantum Learning Theory},
author = {Srinivasan Arunachalam and Ronald de Wolf},
journal= {arXiv preprint arXiv:1701.06806},
year = {2017}
}
Comments
26 pages LaTeX. v2: many small changes to improve the presentation. This version will appear as Complexity Theory Column in SIGACT News in June 2017. v3: fixed a small ambiguity in the definition of gamma(C) and updated a reference