Functional Tensor-Train Chebyshev Method for Multidimensional Quantum Dynamics Simulations

被引:18
|
作者
Soley, Micheline B. [1 ,2 ]
Bergold, Paul [3 ]
Gorodetsky, Alex A. [4 ]
Batista, Victor S. [1 ,2 ,5 ]
机构
[1] Yale Univ, Yale Quantum Inst, New Haven, CT 06520 USA
[2] Yale Univ, Dept Chem, New Haven, CT 06520 USA
[3] Tech Univ Munich, Zentrum Math, D-85748 Garching, Germany
[4] Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA
[5] Yale Univ, Energy Sci Inst, West Haven, CT 06516 USA
关键词
MATRIX RENORMALIZATION-GROUP; SCHRODINGER-EQUATION; MATCHING-PURSUIT; COHERENT-CONTROL; APPROXIMATION; PROPAGATION; DENSITY; OPTIMIZATION; PROBABILITY; FORMULATION;
D O I
10.1021/acs.jctc.1c00941
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Methods for efficient simulations of multidimensional quantum dynamics are essential for theoretical studies of chemical systems where quantum effects are important, such as those involving rearrangements of protons or electronic configurations. Here, we introduce the functional tensor-train Chebyshev (FTTC) method for rigorous nuclear quantum dynamics simulations. FTTC is essentially the Chebyshev propagation scheme applied to the initial state represented in a continuous analogue tensor-train format. We demonstrate the capabilities of FTTC as applied to simulations of proton quantum dynamics in a 50-dimensional model of hydrogen-bonded DNA base pairs.
引用
收藏
页码:25 / 36
页数:12
相关论文
共 50 条
  • [1] Tensor-Train Split-Operator KSL (TT-SOKSL) Method for Quantum Dynamics Simulations
    Lyu, Ningyi
    Soley, Micheline B.
    Batista, Victor S.
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2022, 18 (06) : 3327 - 3346
  • [2] Tensor-Train Split-Operator Fourier Transform (TT-SOFT) Method: Multidimensional Nonadiabatic Quantum Dynamics
    Greene, Samuel M.
    Batista, Victor S.
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2017, 13 (09) : 4034 - 4042
  • [3] Tensor-Train networks for learning predictive modeling of multidimensional data
    da Costa, Nazareth
    Attux, Romis
    Cichocki, Andrzej
    Romano, Joao M. T.
    NEUROCOMPUTING, 2025, 637
  • [4] Survey of the hierarchical equations of motion in tensor-train format for non-Markovian quantum dynamics
    Etienne Mangaud
    Amine Jaouadi
    Alex Chin
    Michèle Desouter-Lecomte
    The European Physical Journal Special Topics, 2023, 232 : 1847 - 1869
  • [5] Survey of the hierarchical equations of motion in tensor-train format for non-Markovian quantum dynamics
    Mangaud, Etienne
    Jaouadi, Amine
    Chin, Alex
    Desouter-Lecomte, Michele
    EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 2023, 232 (12): : 1847 - 1869
  • [6] Tensor-train compression of discrete element method simulation data
    De, Saibal
    Corona, Eduardo
    Jayakumar, Paramsothy
    Veerapaneni, Shravan
    JOURNAL OF TERRAMECHANICS, 2024, 113
  • [7] Gradient-based optimization for regression in the functional tensor-train format
    Gorodetsky, Alex A.
    Jakeman, John D.
    JOURNAL OF COMPUTATIONAL PHYSICS, 2018, 374 : 1219 - 1238
  • [8] Finite temperature quantum dynamics of complex systems: Integrating thermo-field theories and tensor-train methods
    Borrelli, Raffaele
    Gelin, Maxim F.
    WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE, 2021, 11 (06)
  • [9] TTSR: Tensor-Train Subspace Representation Method for Visual Domain Adaptation
    Li, Guorui
    Xu, Pengfei
    Peng, Sancheng
    Wang, Cong
    Cai, Yi
    Yu, Shui
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2024, 36 (11) : 7229 - 7241
  • [10] Subspace method for multiparameter-eigenvalue problems based on tensor-train representations
    Ruymbeek, Koen
    Meerbergen, Karl
    Michiels, Wim
    NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, 2022, 29 (05)