%0 Journal Article
%J Advances in Neural Information Processing Systems (NIPS)
%D 2011
%T Universal low-rank matrix recovery from Pauli measurements
%A Yi-Kai Liu
%X We study the problem of reconstructing an unknown matrix M of rank r and dimension d using O(rd poly log d) Pauli measurements. This has applications in quantum state tomography, and is a non-commutative analogue of a well-known problem in compressed sensing: recovering a sparse vector from a few of its Fourier coefficients. We show that almost all sets of O(rd log^6 d) Pauli measurements satisfy the rank-r restricted isometry property (RIP). This implies that M can be recovered from a fixed ("universal") set of Pauli measurements, using nuclear-norm minimization (e.g., the matrix Lasso), with nearly-optimal bounds on the error. A similar result holds for any class of measurements that use an orthonormal operator basis whose elements have small operator norm. Our proof uses Dudley's inequality for Gaussian processes, together with bounds on covering numbers obtained via entropy duality.
%B Advances in Neural Information Processing Systems (NIPS)
%P 1638-1646
%8 2011/03/14
%G eng
%U http://arxiv.org/abs/1103.2816v2
%! Advances in Neural Information Processing Systems (NIPS) 24