1 citations · 1 across the 4 of their papers we have counts for
4 papers
Online Riemannian Gradient Descent for Quantum State Tomography with Matrix Product Operators
Jian-Feng Cai, Jingyang Li, Xiaoqun Zhang +1
Matrix product operators (MPOs) provide a scalable approach for quantum state tomography (QST) by offering a compact representation of many-body mixed states with limited entanglem…
Fast and Provable Tensor-Train Format Tensor Completion via Precondtioned Riemannian Gradient Descent
Fengmiao Bian, Jian-Feng Cai, Xiaoqun Zhang +1
Low-rank tensor completion aims to recover a tensor from partially observed entries, and it is widely applicable in fields such as quantum computing and image processing. Due to th…
A Single-Mode Quasi Riemannian Gradient Descent Algorithm for Low-Rank Tensor Recovery
Yuanwei Zhang, Ya-Nan Zhu, Xiaoqun Zhang
This paper focuses on recovering a low-rank tensor from its incomplete measurements. We propose a novel algorithm termed the Single Mode Quasi Riemannian Gradient Descent (SM-QRGD)…
Compressing MIMO Channel Submatrices with Tucker Decomposition: Enabling Efficient Storage and Reducing SINR Computation Overhead
Yuanwei Zhang, Ya-Nan Zhu, Xiaoqun Zhang
Massive multiple-input multiple-output (MIMO) systems employ a large number of antennas to achieve gains in capacity, spectral efficiency, and energy efficiency. However, the large…