New arXiv Paper by Miyakoshi et al.
A new paper on variational quantum SVD has been posted on arXiv.
Hiroshi Ueda
Associate Professor
Hiroshi Ueda is an Associate Professor at the Center for Quantum Information and Quantum Biology (QIQB), The University of Osaka.
His research focuses on tensor network methods, quantum many-body physics, quantum algorithms, and quantum-classical hybrid computation. He develops theoretical and numerical approaches for understanding quantum many-body systems and for designing quantum algorithms inspired by tensor network structures.
High-Precision Variational Quantum SVD via Classical Orthogonality Correction
Hybrid quantum-classical variational SVD with orthogonality correction for entanglement spectrum estimation.
Invited Talk at the 14th Workshop on Quantum Many-Body Computation
Hiroshi Ueda delivered an invited talk in Beijing on variational quantum partial SVD and entanglement spectrum estimation.
Visiting Students Joined the Ueda Group
We welcome visiting students from EPFL, the University of Toronto, and UPV/EHU.
Ueda Group Website Launched
The official website of the Ueda Group has been launched.
Quantum-Inspired Algorithm for Classical Spin Hamiltonians Based on Matrix Product Operators
Quantum-Inspired Algorithm for Classical Spin Hamiltonians Based on Matrix Product Operators
ryo-watanabe
Large-scale quantum device benchmarking via LXEB with particle-number-conserving random quantum circuits
Large-scale quantum device benchmarking via LXEB with particle-number-conserving random quantum circuits
takumi-kaneda
TTNOpt: Tree tensor network package for high-rank tensor compression
A tree tensor network software package for quantum spin systems and high-rank tensor compression.
ryo-watanabe
Site-Order Optimization in the Density Matrix Renormalization Group via Multi-Site Rearrangement
Site-Order Optimization in the Density Matrix Renormalization Group via Multi-Site Rearrangement
ryo-watanabe
Improving the accuracy of the tree-tensor network approach by optimization of network structure
Improving the accuracy of the tree-tensor network approach by optimization of network structure
toshiya-hikihara