On learning finite-state quantum sources
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We examine the complexity of learning the distributions produced by finite-state quantum sources. We show how prior techniques for learning hidden Markov models can be adapted to the {\em quantum generator} model to find that the analogous state of affairs holds: information-theoretically, a polynomial number of samples suffice to approximately identify the distribution, but computationally, the problem is as hard as learning parities with noise, a notorious open question in computational learning theory.
2005 ◽
Vol 50
(4)
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pp. 505-511
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2015 ◽
Vol 9
(1)
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pp. 717-752
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2015 ◽
Vol 26
(1-2)
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pp. 61-71
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