TL;DR
This paper explores the properties of quantum states within decision tree structures, examining how quantum correlations can be integrated into classical decision trees and proposing methods for their approximation.
Contribution
It introduces a framework for mapping quantum correlations onto decision trees and provides classical representations and approximations for quantum states in this context.
Findings
Quantum correlations can be embedded in decision tree structures.
Classical approximations of quantum states are feasible within decision trees.
The paper offers methods to analyze and represent quantum decision systems.
Abstract
Quantum decision systems are being increasingly considered for use in artificial intelligence applications. Classical and quantum nodes can be distinguished based on certain correlations in their states. This paper investigates some properties of the states obtained in a decision tree structure. How these correlations may be mapped to the decision tree is considered. Classical tree representations and approximations to quantum states are provided.
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Quantum Computing Algorithms and Architecture · Neural Networks and Applications