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Proceedings Paper

Quantum probabilistic logic programming
Author(s): Radhakrishnan Balu
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Paper Abstract

We describe a quantum mechanics based logic programming language that supports Horn clauses, random variables, and covariance matrices to express and solve problems in probabilistic logic. The Horn clauses of the language wrap random variables, including infinite valued, to express probability distributions and statistical correlations, a powerful feature to capture relationship between distributions that are not independent. The expressive power of the language is based on a mechanism to implement statistical ensembles and to solve the underlying SAT instances using quantum mechanical machinery. We exploit the fact that classical random variables have quantum decompositions to build the Horn clauses. We establish the semantics of the language in a rigorous fashion by considering an existing probabilistic logic language called PRISM with classical probability measures defined on the Herbrand base and extending it to the quantum context. In the classical case H-interpretations form the sample space and probability measures defined on them lead to consistent definition of probabilities for well formed formulae. In the quantum counterpart, we define probability amplitudes on Hinterpretations facilitating the model generations and verifications via quantum mechanical superpositions and entanglements. We cast the well formed formulae of the language as quantum mechanical observables thus providing an elegant interpretation for their probabilities. We discuss several examples to combine statistical ensembles and predicates of first order logic to reason with situations involving uncertainty.

Paper Details

Date Published: 21 May 2015
PDF: 10 pages
Proc. SPIE 9500, Quantum Information and Computation XIII, 950011 (21 May 2015); doi: 10.1117/12.2176986
Show Author Affiliations
Radhakrishnan Balu, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 9500:
Quantum Information and Computation XIII
Eric Donkor; Andrew R. Pirich; Michael Hayduk, Editor(s)

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