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.
Quantum state tomography via compressed sensing
2 Pith papers cite this work. Polarity classification is still indexing.
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Augments quantum state tomography with anytime-valid confidence sequences so that confidence sets contain the true state with user-specified probability at any stopping time.
citing papers explorer
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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.
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Anytime-Valid Quantum State Tomography via Confidence Sequences
Augments quantum state tomography with anytime-valid confidence sequences so that confidence sets contain the true state with user-specified probability at any stopping time.