Monras, Alex ;
Winter, Andreas
Quantum Learning of Classical Stochastic Processes: The CompletelyPositive Realization Problem
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) [Benvenuti,Farina(2004)] and constitutes a central problem in machine learning. The PRP and its solutions have farreaching consequences in many areas of systems and control theory, and is nowadays an important piece in the broad field of positive systems theory [Luenberger(1979)].
We consider the scenario where the latent variables are quantum (e.g., quantum states of a finitedimensional 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 instrumentif anyyields the process at hand by iterative application.
We take as a starting point the theory of quasirealizations, 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 CompletelyPositive 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, deviceindependent characterization and reverseengineering of stochastic processes and quantum processors, and more generally, of dynamical processes with quantum memory [Guta(2011), Guta&Yamamoto(2013)].
BibTeX  Entry
@InProceedings{monras_et_al:LIPIcs:2014:4810,
author = {Alex Monras and Andreas Winter},
title = {{Quantum Learning of Classical Stochastic Processes: The CompletelyPositive Realization Problem}},
booktitle = {9th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2014)},
pages = {99109},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {9783939897736},
ISSN = {18688969},
year = {2014},
volume = {27},
editor = {Steven T. Flammia and Aram W. Harrow},
publisher = {Schloss DagstuhlLeibnizZentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2014/4810},
URN = {urn:nbn:de:0030drops48100},
doi = {10.4230/LIPIcs.TQC.2014.99},
annote = {Keywords: quantum instrument, hidden Markov model, machine learning, quantum measurement}
}
11.12.2014
Keywords: 

quantum instrument, hidden Markov model, machine learning, quantum measurement 
Seminar: 

9th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2014)

Issue date: 

2014 
Date of publication: 

11.12.2014 