Representation of binary classification trees with binary features by quantum circuits
This work addresses the challenge of implementing machine learning models on quantum hardware, though it appears incremental as it adapts existing decision tree concepts to a quantum framework.
The authors tackled the problem of representing binary classification trees with binary features using quantum circuits, achieving the first realization of a decision tree classifier on a quantum device with an on-demand sampling method that enables predictions with constant classical memory independent of tree depth.
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.