A survey of quantum learning theory

Arunachalam, Srinivasan; de Wolf, Ronald

doi:10.1145/3106700.3106710

S. Arunachalam (Srinivasan) and R.M. de Wolf (Ronald)

2017-06-01

A survey of quantum learning theory

Computational Complexity Guest Column , Volume 48 - Issue 2 p. 41- 67

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 learn- ing: exact learning from membership queries, and Probably Approximately Correct (PAC) and agnostic learning from classical or quantum examples.

Additional Metadata
Keywords	Quantum physics, Computational complexity, Machine learning
Persistent URL	doi.org/10.1145/3106700.3106710
Journal	Computational Complexity Guest Column
Grant	This work was funded by the European Commission 7th Framework Programme; grant id erc/615307 - Progress in quantum computing: Algorithms, communication, and applications (QPROGRESS)
Organisation	Algorithms and Complexity
Citation APA Style AAA Style APA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Arunachalam, S., & de Wolf, R. (2017). A survey of quantum learning theory. ACM SIGACT News, 48(2), 41–67. doi:10.1145/3106700.3106710

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