A Survey of Quantum Learning Theory
arXiv:1701.06806v380 citations
Originality Synthesis-oriented
AI Analysis
It provides a comprehensive overview for researchers in quantum computing and machine learning, but it is incremental as a survey.
This paper surveys quantum learning theory, covering theoretical aspects of machine learning with quantum computers, including results for exact learning from membership queries and PAC and agnostic learning from examples.
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.