  Here we utilize a low-rank tensor model (LTM) as a function approximator,
combined with the gradient descent method, to solve eigenvalue problems
including the Laplacian operator and the harmonic oscillator. Experimental
results show the superiority of the polynomial-based low-rank tensor model
(PLTM) compared to the tensor neural network (TNN). We also test such low-rank
architectures for the classification problem on the MNIST dataset.
