Quantum Information Processing

, Volume 13, Issue 3, pp 757–770 | Cite as

Quantum decision tree classifier

  • Songfeng Lu
  • Samuel L. Braunstein


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.


Quantum information processing Quantum entropy Quantum decision tree Quantum classification Machine learning 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.Department of Computer ScienceUniversity of YorkYorkUK

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