Learning Quantum Graphical Models using Constrained Gradient Descent on the Stiefel Manifold

9 Mar 2019Sandesh AdhikarySiddarth SrinivasanByron Boots

Quantum graphical models (QGMs) extend the classical framework for reasoning about uncertainty by incorporating the quantum mechanical view of probability. Prior work on QGMs has focused on hidden quantum Markov models (HQMMs), which can be formulated using quantum analogues of the sum rule and Bayes rule used in classical graphical models... (read more)

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