TTNOpt: Tree tensor network package for high-rank tensor compression
TTNOpt is a software package that utilizes tree tensor networks (TTNs) for quantum spin systems and high-dimensional data analysis.
The package provides efficient TTN computations by locally optimizing the network structure, guided by the entanglement pattern of the target tensors. For quantum spin systems, TTNOpt searches for ground states of Hamiltonians with bilinear spin interactions and magnetic fields, and computes physical properties such as variational energy, bipartite entanglement entropy, single-site expectation values, and two-site correlation functions.
For high-dimensional data analysis, TTNOpt factorizes complex tensors into TTN states by optimizing both tensors and network structures.
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.