Search Results for author:
Found 3 papers, 0 papers with code
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
We address this gap by showing how stationary or uniform versions of popular quantum tensor network models have equivalent representations in the stochastic processes and weighted automata literature, in the limit of infinitely long sequences.
Expressiveness and Learning of Hidden Quantum Markov Models
Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in the development of hidden quantum Markov models (HQMMs) to model stochastic processes.
Learning Quantum Graphical Models using Constrained Gradient Descent on the Stiefel Manifold
Quantum graphical models (QGMs) extend the classical framework for reasoning about uncertainty by incorporating the quantum mechanical view of probability.
