Learning using the Born Rule
نویسنده
چکیده
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 unravel a new probabilistic justification for popular algebraic algorithms, based on the Born rule. These algorithms include two-class and multiple-class spectral clustering, and algorithms based on Euclidean distances.
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