Learning using the Born Rule
Lior Wolf · DSpace@MIT (Massachusetts Institute of Technology) · 2006
In Quantum Mechanics the transition from a deterministic description to a probabilistic one is done using a simple rule termed the Born rule. This rule states that the probability of an outcome (a) given a state (Ψ) is the square of their inner products ((a ⊤ Ψ) 2). In this paper, we will explore the use of the Born-rule-based probabilities for clustering, feature selection, classification, and for comparison between sets. We show how these probabilities lead to existing and new algebraic algorithms for which no other complete probabilistic justification is known.