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Found 3 papers, 1 papers with code
Tangent-Space Gradient Optimization of Tensor Network for Machine Learning
The gradient-based optimization method for deep machine learning models suffers from gradient vanishing and exploding problems, particularly when the computational graph becomes deep.
Quantum Compressed Sensing with Unsupervised Tensor-Network Machine Learning
To transfer a specific piece of information with $|\Psi \rangle$, our proposal is to encode such information in the separable state with the minimal distance to the measured state $|\Phi \rangle$ that is obtained by partially measuring on $|\Psi \rangle$ in a designed way.
Generative Tensor Network Classification Model for Supervised Machine Learning
By investigating the distances in the many-body Hilbert space, we find that (a) the samples are naturally clustering in such a space; and (b) bounding the bond dimensions of the TN's to finite values corresponds to removing redundant information in the image recognition.
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