On Quantum Decision Trees
This work addresses the integration of quantum systems into AI decision-making, but it appears incremental as it focuses on theoretical properties without clear practical applications.
The paper investigates properties of quantum states in decision tree structures and provides classical representations and approximations for mapping correlations to decision trees.
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.