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Found 3 papers, 2 papers with code
Tensor networks for unsupervised machine learning
Despite the great potential, however, existing tensor network models for unsupervised machine learning only work as a proof of principle, as their performance is much worse than the standard models such as restricted Boltzmann machines and neural networks.
Boltzmann machines as two-dimensional tensor networks
Using numerical experiments, we demonstrate that the proposed algorithm is much more accurate than the state-of-the-art machine learning methods in estimating the partition function of restricted Boltzmann machines and deep Boltzmann machines, and have potential applications in training deep Boltzmann machines for general machine learning tasks.
Contracting Arbitrary Tensor Networks: general approximate algorithm and applications in graphical models and quantum circuit simulations
We present a general method for approximately contracting tensor networks with an arbitrary connectivity.
Computational Physics Statistical Mechanics Strongly Correlated Electrons Quantum Physics
