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Unrolling SVT to obtain computationally efficient SVT for n-qubit quantum state tomography
Existing works focus on estimating the density matrix that represents the state, using a compressive sensing approach, with only fewer measurements than that required for a tomographically complete set, with the assumption that the true state has a low rank.
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE
Affine rank minimization problem is the generalized version of low rank matrix completion problem where linear combinations of the entries of a low rank matrix are observed and the matrix is estimated from these measurements.
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