An online Riemannian gradient descent method for MPO-based quantum state tomography achieves linear convergence with quadratically scaling sample complexity and connects the problem to low TT-rank tensor completion.
Sample-efficient quantum state tomography for structured quantum states in one dimension
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
quant-ph 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A unified structured factorization framework for quantum state tomography that parametrizes the density matrix as FF^dagger, supports multiple priors, provides sample complexity bounds, and introduces projected gradient descent and power-method algorithms.
A survey of structured quantum state tomography covering compact representations, measurement design, and optimization algorithms, connected to compressive sensing for sample efficiency.
citing papers explorer
-
Online Riemannian Gradient Descent for Quantum State Tomography with Matrix Product Operators
An online Riemannian gradient descent method for MPO-based quantum state tomography achieves linear convergence with quadratically scaling sample complexity and connects the problem to low TT-rank tensor completion.
-
Structured Factorization Approaches for Quantum State Tomography
A unified structured factorization framework for quantum state tomography that parametrizes the density matrix as FF^dagger, supports multiple priors, provides sample complexity bounds, and introduces projected gradient descent and power-method algorithms.
-
Statistical and Algorithmic Foundations of Probing Quantum Systems with Compressive Measurements: A Review
A survey of structured quantum state tomography covering compact representations, measurement design, and optimization algorithms, connected to compressive sensing for sample efficiency.