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.
Provable tensor-train format tensor completion by riemannian optimization,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
An overview revisits LoRA variants by categorizing advances in architectural design, efficient optimization, and applications while linking them to classical signal processing tools for principled fine-tuning.
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.
-
Low-Rank Adaptation Redux for Large Models
An overview revisits LoRA variants by categorizing advances in architectural design, efficient optimization, and applications while linking them to classical signal processing tools for principled fine-tuning.