2021
DOI: 10.1103/physrevb.104.214302
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Efficient bond-adaptive approach for finite-temperature open quantum dynamics using the one-site time-dependent variational principle for matrix product states

Abstract: Recent tensor network techniques for simulating system-environment wavefunctions have provided profound insights into non-Markovian dissipation and decoherence in open quantum systems. Here, we propose a dynamically adaptive one-site Time-Dependent-Variational-Principle (A1TDVP) method for matrix product states in which local bond dimensions grow to capture developing systembath entanglement. This avoids the need for multiple convergence runs w.r.t. bond dimensions and the unfavourable local Hilbert space scal… Show more

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Cited by 26 publications
(17 citation statements)
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“…The 1TDVP has a larger projection error than the 2TDVP, and the standard 1TDVP cannot increase the bond dimensions of the MPS during time evolution to meet the entanglement growth. To improve this, some studies embed the dynamical bond dimension into the 1TDVP. For real calculations, it is important to combine the different implementations of the TDVP.…”
Section: Methodsmentioning
confidence: 99%
“…The 1TDVP has a larger projection error than the 2TDVP, and the standard 1TDVP cannot increase the bond dimensions of the MPS during time evolution to meet the entanglement growth. To improve this, some studies embed the dynamical bond dimension into the 1TDVP. For real calculations, it is important to combine the different implementations of the TDVP.…”
Section: Methodsmentioning
confidence: 99%
“…However, compared to the standard one‐site algorithm, the strict TDVP is violated and in addition, the computational scaling is larger. Recently, further development of the one‐site TDVP‐PS algorithm is also able to adaptively optimize the bond dimension, which avoids the high‐computational cost of the two‐site algorithm 123–125 …”
Section: Methodology and Algorithmmentioning
confidence: 99%
“…Recently, further development of the one-site TDVP-PS algorithm is also able to adaptively optimize the bond dimension, which avoids the high-computational cost of the two-site algorithm. [123][124][125] Because the TDVP-VMF/CMF scheme and the TDVP-PS scheme are both based on TDVP, jΨ(t)i should be the same if not considering the error of the integrator. With regard to the time step size, in contrast to P&C-RK, the error will have a monotonic relationship with it.…”
Section: Tdvp-psmentioning
confidence: 99%
“…The ordering of the modes may be an important factor influencing the efficiency of Time Dependent Variational Principle (TDVP) methods [97]. Further improvements are currently underway to consider the reorganization of the modes [72,98], rank adaptative methods [37,56,[99][100][101] or hierarchical tensor train approach [55].…”
Section: Oct By Tensor Trainsmentioning
confidence: 99%