Related papers: Representation of binary classification trees with binary features by quantum circuits

Representation of binary classification trees with binary features by quantum circuits

URL: http://arxiv.org/abs/2108.13207v1
Date: Mon, 30 Aug 2021 13:02:00 GMT
Title: Representation of binary classification trees with binary features by quantum circuits
Authors: Raoul Heese, Patricia Bickert, Astrid Elisa Niederle
Abstract summary: We propose a quantum representation of binary classification trees with binary features based on a probabilistic approach. We experimentally study our approach using both a quantum computing simulator and actual IBM quantum hardware.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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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