A hard-core predicate for all one-way functions
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Learning decision trees using the Fourier spectrum
SIAM Journal on Computing
Boosting a weak learning algorithm by majority
Information and Computation
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
Journal of Computer and System Sciences
Learning DNF over the Uniform Distribution Using a Quantum Example Oracle
SIAM Journal on Computing
More efficient PAC-learning of DNF with membership queries under the uniform distribution
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Quantum computation and quantum information
Quantum computation and quantum information
Modern Cryptography, Probabilistic Proofs, and Pseudorandomness
Modern Cryptography, Probabilistic Proofs, and Pseudorandomness
A Quantum Goldreich-Levin Theorem with Cryptographic Applications
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
Smooth Boosting and Learning with Malicious Noise
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Hard-core distributions for somewhat hard problems
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Quantum versus Classical Learnability
CCC '01 Proceedings of the 16th Annual Conference on Computational Complexity
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We describe a quantum PAC learning algorithm for DNF formulae under the uniform distribution with a query complexity of 脮(s3/驴 + s2/驴2), where s is the size of DNF formula and 驴 is the PAC error accuracy1. If s and 1/驴 are comparable, this gives a modest improvement over a previously known classical query complexity of 脮(ns2/驴2). We also show a lower bound of 驴(s log n/n) on the query complexity of any quantum PAC algorithm for learning a DNF of size s with n inputs under the uniform distribution.