Communications of the ACM
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
SIAM Journal on Computing
Stabilization of Quantum Computations by Symmetrization
SIAM Journal on Computing
Quantum computation and quantum information
Quantum computation and quantum information
Machine Learning
Machine Learning
Machine Learning
Separating Quantum and Classical Learning
ICALP '01 Proceedings of the 28th International Colloquium on Automata, Languages and Programming,
Equivalences and Separations Between Quantum and Classical Learnability
SIAM Journal on Computing
Random Measurement Bases, Quantum State Distinction and Applications to the Hidden Subgroup Problem
CCC '06 Proceedings of the 21st Annual IEEE Conference on Computational Complexity
Quantum speed-up for unsupervised learning
Machine Learning
Quantum adiabatic machine learning
Quantum Information Processing
Quantum decision tree classifier
Quantum Information Processing
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Quantum Information Processing (QIP) performs wonders in a world that obeys the laws of quantum mechanics, whereas Machine Learning (ML) is generally assumed to be done in a classical world. We initiate an investigation of the encounter of ML with QIP by defining and studying novel learning tasks that correspond to Machine Learning in a world in which the information is fundamentally quantum mechanical. We shall see that this paradigm shift has a profound impact on the learning process and that our classical intuition is often challenged.