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arXiv · 2108.13207

Representation of binary classification trees with binary features by quantum circuits

Raoul Heese·Patricia Bickert·Astrid Elisa Niederle
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Abstract

We propose a quantum representation of binary classification trees with binary features based on a probabilistic approach. By using the quantum computer as a processor for probability distributions, a probabilistic traversal of the decision tree can be realized via measurements of a quantum circuit. We describe how tree inductions and the prediction of class labels of query data can be integrated into this framework. An on-demand sampling method enables predictions with a constant number of classical memory slots, independent of the tree depth. We experimentally study our approach using both a quantum computing simulator and actual IBM quantum hardware. To our knowledge, this is the first realization of a decision tree classifier on a quantum device.

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Raoul Heese, Patricia Bickert, Astrid Elisa Niederle. 2021-08-30. Representation of binary classification trees with binary features by quantum circuits. https://doi.org/10.22331/q-2022-03-30-676

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