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Quantum decision tree classifier

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Abstract

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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Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Songfeng Lu

  2. Department of Computer Science, University of York, York, YO10 5GH, UK

    Samuel L. Braunstein

Authors
  1. Songfeng Lu
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  2. Samuel L. Braunstein
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Corresponding author

Correspondence to Songfeng Lu.

Additional information

The work is supported by the National Natural Science Foundation of China under Grant No. 61173050.

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Lu, S., Braunstein, S.L. Quantum decision tree classifier. Quantum Inf Process 13, 757–770 (2014). https://doi.org/10.1007/s11128-013-0687-5

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  • DOI: https://doi.org/10.1007/s11128-013-0687-5

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