Representation of binary classification trees with binary features by quantum circuits

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.

Quantum Capsule Networks

“…With the development and flourishing of quantum computing architecture [17][18][19][20][21][22][23], the interplay between quantum physics and AI has attracted a wide range of interests [24][25][26][27]. Along this line, many heuristic quantum machine learning models have been proposed, including the quantum decision tree classifiers [28,29], quantum support vector machines [30], quantum Boltzmann machines [31], quantum generative models [32][33][34][35], quantum convolutional neural networks [36][37][38][39][40], and perception-based quantum neural networks [41], etc. Some of these works show potential quantum advantages over their classical counterparts, which have boosted the development of quantum AI [25].…”