Journal
QUANTUM INFORMATION PROCESSING
Volume 13, Issue 3, Pages 757-770Publisher
SPRINGER
DOI: 10.1007/s11128-013-0687-5
Keywords
Quantum information processing; Quantum entropy; Quantum decision tree; Quantum classification; Machine learning
Funding
- National Natural Science Foundation of China [61173050]
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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.
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