TY - JOUR
T1 - Universal low-rank matrix recovery from Pauli measurements
JF - Advances in Neural Information Processing Systems (NIPS)
Y1 - 2011
A1 - Yi-Kai Liu
AB - 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.
U4 - 1638-1646
UR - http://arxiv.org/abs/1103.2816v2
J1 - Advances in Neural Information Processing Systems (NIPS) 24
ER -