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Quantum decision tree classifier

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Publication details

JournalQuantum Information Processing
DatePublished - Mar 2014
Issue number3
Number of pages14
Pages (from-to)757-770
Original languageEnglish


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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