Equivalences and Separations Between Quantum and Classical Learnability

@article{Servedio2004EquivalencesAS,
  title={Equivalences and Separations Between Quantum and Classical Learnability},
  author={Rocco A. Servedio and Steven J. Gortler},
  journal={SIAM J. Comput.},
  year={2004},
  volume={33},
  pages={1067-1092}
}
We consider quantum versions of two well-studied models of learning Boolean functions: Angluin’s model of exact learning from membership queries and Valiant’s Probably Approximately Correct (PAC) model of learning from random examples. For each of these two learning models we establish a polynomial relationship between the number of quantum versus classical queries required for learning. These results contrast known results which show that testing black-box functions for various properties, as… CONTINUE READING
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Showing 1-10 of 43 references

M

R. Beals, H. Buhrman, R. Cleve
Mosca and R. de Wolf. Quantum lower bounds by polynomials, in “Proc. 39th IEEE Symp. on Found. of Comp. Sci.,” • 1998
View 8 Excerpts
Highly Influenced

On the Power of Quantum Computation

View 18 Excerpts
Highly Influenced

Cryptographic Limitations on Learning Boolean Formulae and Finite Automata

Machine Learning: From Theory to Applications • 1993
View 8 Excerpts
Highly Influenced

Queries and concept learning

Machine Learning • 1987
View 6 Excerpts
Highly Influenced

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