Quantum learning of classical stochastic processes : The completely-positive realization problem
Monras Blasi, Àlex (Universitat Autònoma de Barcelona. Departament de Física)
Winter, Andreas (Universitat Autònoma de Barcelona. Departament de Física)

Imprint:	Dagstuhl : Leibniz-Zentrum fuer Informatik, 2014
Abstract:	Among several tasks in Machine Learning, is the problem of inferring the latent variables of a system and their causal relations with the observed behavior. A paradigmatic instance of such problem is the task of inferring the Hidden Markov Model underlying a given stochastic process. This is known as the positive realization problem (PRP) [3] and constitutes a central problem in machine learning. The PRP and its solutions have far-reaching consequences in many areas of systems and control theory, and is nowadays an important piece in the broad field of positive systems theory [21]. We consider the scenario where the latent variables are quantum (e. g. , quantum states of a finite-dimensional system), and the system dynamics is constrained only by physical transformations on the quantum system. The observable dynamics is then described by a quantum instrument, and the task is to determine which quantum instrument - if any - yields the process at hand by iterative application. We take as a starting point the theory of quasi-realizations, whence a description of the dynamics of the process is given in terms of linear maps on state vectors and probabilities are given by linear functionals on the state vectors. This description, despite its remarkable resemblance with the Hidden Markov Model, or the iterated quantum instrument, is however devoid from any stochastic or quantum mechanical interpretation, as said maps fail to satisfy any positivity conditions. The Completely-Positive realization problem then consists in determining whether an equivalent quantum mechanical description of the same process exists. We generalize some key results of stochastic realization theory, and show that the problem has deep connections with operator systems theory, giving possible insight to the lifting problem in quotient operator systems. Our results have potential applications in quantum machine learning, device-independent characterization and reverse-engineering of stochastic processes and quantum processors, and more generally, of dynamical processes with quantum memory [16, 17].
Grants:	Ministerio de Ciencia e Innovación FIS2008-01236
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Series:	Leibniz International Proceedings in Informatics (LIPIcs) ; 27
Document:	Capítol de llibre ; recerca ; Versió publicada
Subject:	Quantum instrument ; Hidden Markov model ; Machine learning ; Quantum measurement
Published in:	9th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2014), 2014, p. 99-109, ISBN 978-3-939897-73-6

DOI: 10.4230/LIPIcs.TQC.2014.99

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