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COCO
2001
Springer

Quantum versus Classical Learnability

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Quantum versus Classical Learnability
Motivated by recent work on quantum black-box query complexity, we consider quantum versions of two wellstudied 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. Our results provide an interesting contrast to known results which show that testing blackbox functions for various properties can require exponentially more classical queries than quantum queries. We also show that under a widely held computational hardness assumption there is a class of Boolean functions which is polynomial-time learnable in the quantum version but not the classical version of each learning model; thus while quantum and classical learning are equally powerful from an information theory perspective, they are different...
Rocco A. Servedio, Steven J. Gortler
Added 28 Jul 2010
Updated 28 Jul 2010
Type Conference
Year 2001
Where COCO
Authors Rocco A. Servedio, Steven J. Gortler
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