Quantum learning of classical stochastic processes: The completely positive realization problem

DOI10.1063/1.4936935zbMath1333.81212arXiv1412.3634OpenAlexW2170835772WikidataQ57521804 ScholiaQ57521804MaRDI QIDQ2786625

Alex Monras, Andreas Winter

Publication date: 15 February 2016

Published in: Journal of Mathematical Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1412.3634

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05) Quantum computation (81P68) Operator spaces and completely bounded maps (46L07) Quantum stochastic calculus (81S25) Diagnostics, and linear inference and regression (62J20) Quantum control (81Q93) Inference from stochastic processes and fuzziness (62M86)

Related Items (1)

Quantum theory in finite dimension cannot explain every general process with finite memory

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The complete realization problem for hidden Markov models: a survey and some new results
• Finitely correlated states on quantum spin chains
• On some questions of rationality and decidability
• Noncommutative Markov chains associated to a preassigned evolution: an application to the quantum theory of measurement
• The realization problem for hidden Markov models
• Quotients, exactness, and nuclearity in the operator system category
• Positive matrix factorization via extremal polyhedral cones
• Separability of mixed states: necessary and sufficient conditions.
• Equivalence classes and local asymptotic normality in system identification for quantum Markov chains
• Attempt of an axiomatic foundation fo quantum mechanics and more general theories. II
• Statistical Structure of Quantum Theory
• Sufficient and Necessary Conditions for Semidefinite Representability of Convex Hulls and Sets
• Separability Criterion for Density Matrices
• Operator system quotients of matrix algebras and their tensor products
• Control of open quantum systems: case study of the central spin model
• Lifts of Convex Sets and Cone Factorizations
• Generalized probabilistic theories and conic extensions of polytopes
• Learning Hidden Markov Models Using Nonnegative Matrix Factorization
• A Tutorial on the Positive Realization Problem
• Linear vs. semidefinite extended formulations
• Irreducible Realizations and the Degree of a Rational Matrix
• Functions of Markov Chains
• Sufficient Conditions for a Stationary Process to be a Function of a Finite Markov Chain

This page was built for publication: Quantum learning of classical stochastic processes: The completely positive realization problem