A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Quantum computation and quantum information
Quantum computation and quantum information
Complexity measures and decision tree complexity: a survey
Theoretical Computer Science - Complexity and logic
IEEE Intelligent Systems
Machine Learning
Pattern Recognition with Quantum Neural Networks
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Quantum Information Processing
Machine learning in a quantum world
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Quantum speed-up for unsupervised learning
Machine Learning
Quantum adiabatic machine learning
Quantum Information Processing
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We study the quantum version of a decision tree classifier to fill the gap between quantum computation and machine learning. The quantum entropy impurity criterion which is used to determine which node should be split is presented in the paper. By using the quantum fidelity measure between two quantum states, we cluster the training data into subclasses so that the quantum decision tree can manipulate quantum states. We also propose algorithms constructing the quantum decision tree and searching for a target class over the tree for a new quantum object.