Separating Quantum and Classical Learning
ICALP '01 Proceedings of the 28th International Colloquium on Automata, Languages and Programming,
Quantum DNF Learnability Revisited
COCOON '02 Proceedings of the 8th Annual International Conference on Computing and Combinatorics
Learning DNF from random walks
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Improved Bounds on Quantum Learning Algorithms
Quantum Information Processing
Quantum Algorithms for Learning and Testing Juntas
Quantum Information Processing
The geometry of quantum learning
Quantum Information Processing
An improved lower bound on query complexity for quantum PAC learning
Information Processing Letters
Quantum interpolation of polynomilas
Quantum Information & Computation
Quantum algorithms for shifted subset problems
Quantum Information & Computation
How many query superpositions are needed to learn?
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Random oracles in a quantum world
ASIACRYPT'11 Proceedings of the 17th international conference on The Theory and Application of Cryptology and Information Security
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We generalize the notion of probably approximately correct (PAC) learning from an example oracle to a notion of efficient learning on a quantum computer using a quantum example oracle. This quantum example oracle is a natural extension of the traditional PAC example oracle, and it immediately follows that all PAC-learnable function classes are learnable in the quantum model. Furthermore, we obtain positive quantum learning results for classes that are not known to be PAC learnable. Specifically, we show that disjunctive normal form (DNF) is efficiently learnable with respect to the uniform distribution by a quantum algorithm using a quantum example oracle. While it was already known that DNF is uniform-learnable using a membership oracle, we prove that a quantum example oracle with respect to uniform is less powerful than a membership oracle.