Communications of the ACM
Matrix analysis
A general lower bound on the number of examples needed for learning
Information and Computation
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
An introduction to computational learning theory
An introduction to computational learning theory
A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
SIAM Journal on Computing
Learning DNF over the Uniform Distribution Using a Quantum Example Oracle
SIAM Journal on Computing
Quantum computation and quantum information
Quantum computation and quantum information
Introduction to Algorithms
Quantum Lower Bounds by Polynomials
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Classical and Quantum Computation
Classical and Quantum Computation
Equivalences and Separations Between Quantum and Classical Learnability
SIAM Journal on Computing
Improved Bounds on Quantum Learning Algorithms
Quantum Information Processing
Algorithms for quantum computation: discrete logarithms and factoring
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
| Hi-index | 0.89 |
In this paper, we study the quantum PAC learning model, offering an improved lower bound on the query complexity. For a concept class with VC dimension d, the lower bound is @W(1@e(d^1^-^e+log(1@d))) for @e accuracy and 1-@d confidence, where e can be an arbitrarily small positive number. The lower bound is very close to the best lower bound known on query complexity for the classical PAC learning model, which is @W(1@e(d+log(1@d))).