Importance

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Abstract

Systems and methods for decision tree construction and update using quantum algorithms are disclosed. A method may include: receiving, by a classical computer program, a dataset comprising a plurality of training examples, each of the plurality of training examples having a plurality of features; loading, by a classical computer program, the dataset into a quantum accessible data structure; providing, by the classical computer program, the quantum accessible data structure to a quantum computer, wherein the quantum computer is configured to perform quantum estimation of a Pearson correlation coefficient on the dataset in the quantum accessible data structure to create a weighted dataset; clustering, by the classical computer program and the quantum computer, each of the plurality of training examples in the weighted dataset into one of a plurality of clusters; and selecting, by the classical computer program, a label for each of the plurality of clusters.

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First Claim