Optimal quantum sample complexity of learning algorithms

Arunachalam, Srinivasan; de Wolf, Ronald

doi:10.4230/LIPIcs.CCC.2017.25

Additional Metadata
Keywords	Agnostic learning, PAC learning, Quantum computing, VC dimension
Persistent URL	doi.org/10.4230/LIPIcs.CCC.2017.25
Series	Leibniz International Proceedings in Informatics, LIPIcs: Proceedings of Conference of Computational Complexity
Project	Progress in quantum computing:Algorithms, communication, and applications
Conference	IEEE Conference on Computational Complexity
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). Optimal quantum sample complexity of learning algorithms. In Computational Complexity Conference (pp. 25:1–25:31). doi:10.4230/LIPIcs.CCC.2017.25