More is different.
God is in the details.
NEWS
 Dates
 March 2529, 2024
 Conference
 SQAINCTS Workshop on Tensor Network and Quantum Embedding (Hongo Campus, The University of Tokyo)
 Title
 Optimizing the structure of tree tensor network for quantum generative modeling using mutual informationbased approach
 Abstract
 Generative modeling is a crucial task in the field of machine learning. Recently, there have been several proposals for generative models on quantum devices. We can efficiently optimize generative models defined by tensor network states, but their performance largely depends on the geometrical structure of the tensor network. To tackle this issue, we have proposed an optimization method for the network structure in the tree tensor network class, based on the least mutual information principle. Generative modeling with an optimized network structure has better performance than a fixed network structure. Moreover, by embedding data dependencies into the tree structure based on the least mutual information principle, we can geometrically represent the correlations in the data.
 Book title
 Advanced Mathematical Science for Mobility Society
 Editors
 Kazushi Ikeda, Yoshiumi Kawamura, Kazuhisa Makino, Satoshi Tsujimoto, Nobuo Yamashita, Shintaro Yoshizawa, Hanna Sumita
 Publisher
 Springer Singapore
 Reference
 ISBN 9789819997718 ISBN 9789819997725 (eBook)
 Title
 Chapter 5 "Application of Tensor Network Formalism for Processing Tensor Data"
 Authors
 Kenji Harada, Hiroaki Matsueda, and Tsuyoshi Okubo
 Web page
 Open access
 Date
 Jan 26, 2024
 Conference
 2024 Annual Meeting of the Physical Society of Taiwan, Topical Symposia:Manybody systems and advanced numerical methods
 Title
 Optimizing tensor network structure
 Date
 Jan 22, 2024
 Conference
 Miniworkshop: Tensor Network algorithms and applications 2024 (Taipei, Taiwan)
 Title
 Optimizing tensor network structure
 Date
 Aug 22, 2023
 Conference
 Tensor Network States: Algorithms and Applications 2023 (Shanghai, China)
 Title
 Tensor network study of onedimensional stochastic processes
 Date
 Aug 8, 2023
 Conference
 The 28th International Conference on Statistical Physics, Statphys28 (Tokyot, Japan)
 Title
 Renormalization of nonequilibrium critical points in onedimensional stochastic processes by tensor networks
 Abstract

Nonequilibrium critical points often hold scaling invariance in the spatial and temporal directions. As seen in equilibrium cases, we know various universality classes of nonequilibrium critical points in stochastic processes. However, the direct renormalization of statistical processes is technically difficult. Recently, renormalization using tensor network representation was proposed and extended[14], and it is quite successful in equilibrium critical points. We extend the approach to stochastic processes using oblique projectors in the tensor renormalization group with higherorder singular value decomposition[5]. We report the universal property of timeevolution operators of onedimensional contact processes of which critical points belong to the (1+1)dimensional directed percolation(DP) universality class. The renormalized timeevolution operator has a universal spectrum structure of the (1+1)dimensional DP universality class in spatial and temporal directions.
[1] M. Levin and C. P. Nave, Tensor Renormalization Group Approach to TwoDimensional Classical Lattice Models, Physical Review Letters 99, 120601 (2007).
[2] Z. Y. Xie, J. Chen, M. P. Qin, J. W. Zhu, Y. P. L., and T. Xiang, CoarseGraining Renormalization by HigherOrder Singular Value Decomposition, Physical Review B 86, 045139 (2012).
[3] G. Evenbly and G. Vidal, Tensor Network Renormalization, Physical Review Letters 115, 180405 (2015).
[4] K. Harada, Entanglement Branching Operator, Physical Review B 97, 045124 (2018).
[5] K. Harada, Universal spectrum structure at nonequilibrium critical points in the (1+1)dimensional directed percolation, arXiv:2008.10807.
TOPICS
Toolkit of Bayesian Scaling Analysis
Reference application software of a new scaling analysis method of critical phenomena based on Bayesian inference.
To demo To detailsMonte Carlo simulations
This demonstration shows a Monte Carlo simulation of the twodimensional Ising model by three algorithms: Metropolis, SwendsenWang, and Wolff algorithms.
To demoABOUT
Kenji Harada
(
原田健自
)
Assistant Professor,
Graduate School of Informatics, Kyoto University, Japan.
harada.kenji.8e@kyotou.ac.jp
Room 203, Research Bldg. No.8, Yoshida Campus, Kyoto Univ., Kyoto, 6068501, Japan.
Map (No.59)