Maxime Meyer, Soumik Adhikary, Naixu Guo, Patrick Rebentrost (Feb 04 2025).
Abstract: Quantum state tomography, the task of learning an unknown quantum state, is a fundamental problem in quantum information. In standard settings, the complexity of this problem depends significantly on the type of quantum state that one is trying to learn, with pure states being substantially easier to learn than general mixed states. A natural question is whether this separation holds for any quantum state learning setting. In this work, we consider the online learning framework and prove the surprising result that learning pure states in this setting is as hard as learning mixed states. More specifically, we show that both classes share almost the same sequential fat-shattering dimension, leading to identical regret scaling under the
L1-loss. We also generalize previous results on full quantum state tomography in the online setting to learning only partially the density matrix, using smooth analysis.
