#ShannonNyquist

Quantum Sparse Recovery and Quantum Orthogonal Matching Pursuit We study quantum sparse recovery in non-orthogonal, overcomplete dictionaries: given coherent quantum access to a state and a dictionary of vectors, the goal is to reconstruct the state up to $\ell_2$...

This mirrors the classical #CompressedSensing vs. #ShannonNyquist setting: lower bounds for dense objects stay intact, but sparsity in the right dictionary changes the sample/query complexity.

A huge thanks to Stefano Vanerio, @raistolo.bsky.social, and to everyone who discussed this with me. 6/6