Representation of binary classification trees with binary features by quantum circuits
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.
Code (1)
Tasks
Binary ClassificationSimilar Papers 제목 키워드 기반
On Ternary Coding and Three-Valued Logic
Mathematically, ternary coding is more efficient than binary coding. It is little used in computation because technology for binary processing is already established and the implementation of ternary coding is more compl…
General ClassificationMixed integer linear optimization formulations for learning optimal binary classification trees
Decision trees are powerful tools for classification and regression that attract many researchers working in the burgeoning area of machine learning. One advantage of decision trees over other methods is their interpreta…
Binary ClassificationClassificationLearning Binary Trees via Sparse Relaxation
One of the most classical problems in machine learning is how to learn binary trees that split data into meaningful partitions. From classification/regression via decision trees to hierarchical clustering, binary trees a…
ClusteringregressionPhylo2Vec: a vector representation for binary trees
Binary phylogenetic trees inferred from biological data are central to understanding the shared history among evolutionary units. However, inferring the placement of latent nodes in a tree is computationally expensive. S…
dtControl: Decision Tree Learning Algorithms for Controller Representation
Decision tree learning is a popular classification technique most commonly used in machine learning applications. Recent work has shown that decision trees can be used to represent provably-correct controllers concisely.…