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A Survey of Quantum Learning Theory

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arxiv 1701.06806 v3 pith:KMR4SZGW submitted 2017-01-24 quant-ph cs.CCcs.LG

Srinivasan Arunachalam (CWI) , Ronald de Wolf (CWI , U of Amsterdam) This is my paper · ORCID
classification quant-phcs.CCcs.LG
keywords learningquantumtheoryagnosticapproximatelyaspectsclassicalcomputers
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Lie-Equivariant Quantum Graph Neural Networks

    quant-ph 2024-11 conditional novelty 6.0 of 10

    A Lorentz-equivariant quantum graph neural network matches the classical LorentzNet on quark-gluon jet discrimination in noiseless simulations.

  2. Rethinking quantum information in gravity and fields

    hep-th 2026-06 unverdicted novelty 2.0 of 10

    The paper organizes important open questions in quantum gravity and quantum information into four themes without presenting new results or derivations.

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