A Survey of Quantum Learning Theory

TL;DR

This survey reviews the theoretical foundations of quantum learning, covering models like exact learning, PAC, and agnostic learning, highlighting key results in quantum machine learning theory.

Contribution

It provides a comprehensive overview of quantum learning theory, summarizing existing results across different models and comparing classical and quantum approaches.

Findings

Summarizes main results in quantum learning models.

Highlights differences between classical and quantum learning.

Identifies open problems in quantum learning theory.

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.