Representation of binary classification trees with binary features by quantum circuits

TL;DR

This paper introduces a novel quantum approach to representing and executing binary classification trees with binary features, enabling efficient predictions using quantum circuits and minimal classical memory.

Contribution

It presents the first implementation of a decision tree classifier on quantum hardware, integrating tree induction and prediction within a probabilistic quantum framework.

Findings

Successful simulation on quantum hardware and simulators

Constant classical memory usage for predictions

First quantum realization of a decision tree classifier

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.