Euclidean Distance Between Haar Orthogonal and Gaussian Matrices

In this work, we study a version of the general question of how well a Haar-distributed orthogonal matrix can be approximated by a random Gaussian matrix. Here, we consider a Gaussian random matrix (Formula presented.) of order n and apply to it the Gram–Schmidt orthonormalization procedure by columns to obtain a Haar-distributed orthogonal matrix (Formula presented.). If (Formula presented.) denotes the vector formed by the first m-coordinates of the ith row of (Formula presented.) and (Formula presented.), our main result shows that the Euclidean norm of (Formula presented.) converges exponentially fast to (Formula presented.), up to negligible terms. To show the extent of this result, we use it to study the convergence of the supremum norm (Formula presented.) and we find a coupling that improves by a factor (Formula presented.) the recently proved best known upper bound on (Formula presented.). Our main result also has applications in Quantum Information Theory.

Wavefront reconstruction from tangential and sagittal curvature

In a previous contribution [Appl. Opt. 51, 8599 (2012)], a coauthor of this work presented a method for reconstructing the wavefront aberration from tangential refractive power data measured using dynamic skiascopy. Here we propose a new regularized least squares method where the wavefront is reconstructed not only using tangential but also sagittal curvature data. We prove that our new method provides improved quality reconstruction for typical and also for highly aberrated wavefronts, under a wide range of experimental error levels. Our method may be applied to any type of wavefront sensor (not only dynamic skiascopy) able to measure either just tangential or tangential plus sagittal curvature data.