A Further Comparison of TD-DMRG and ML-MCTDH for Nonadiabatic Dynamics of Exciton Dissociation

• URL: http://arxiv.org/abs/2509.00456v2
• Date: Tue, 09 Sep 2025 09:50:05 GMT
• Title: A Further Comparison of TD-DMRG and ML-MCTDH for Nonadiabatic Dynamics of Exciton Dissociation
• Authors: Weitang Li, Jiajun Ren, Jun Yan,
• Abstract summary: A recent study reported up to 60% discrepancy in their calculations for excitonconfiguration.<n>By revisiting the benchmark P3HT:PCBM heterojunction model, we show that the observed discrepancies arise primarily from insufficient bond dimensions.<n>Our results confirm both methods converge to numerically exact solutions when bond dimensions are adequately scaled.
• Score: 2.0480965608306305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tensor network methods, such as time-dependent density matrix renormalization group (TD-DMRG) and multi-layer multiconfiguration time-dependent Hartree (ML-MCTDH), are powerful tools for simulating quantum dynamics. While both methods are theoretically exact in the limit of large bond dimensions, a recent study reported up to 60% discrepancy in their calculations for exciton dissociation. To resolve this inconsistency, we conduct a systematic comparison using Renormalizer, a unified software framework for matrix product states (MPS) and tree tensor network states (TTNS). By revisiting the benchmark P3HT:PCBM heterojunction model, we show that the observed discrepancies arise primarily from insufficient bond dimensions. By increasing bond dimensions, we first reduce the difference to less than 10%. Further refinement using an extrapolation scheme and an optimized tensor network structure lowers the difference to approximately 2%. Our results confirm both methods converge to numerically exact solutions when bond dimensions are adequately scaled. This work not only validates the reliability of both methods but also provides high-accuracy benchmark data for future developments in quantum dynamics simulations.

Related papers

• Multi-Dimensional Visual Data Recovery: Scale-Aware Tensor Modeling and Accelerated Randomized Computation [51.65236537605077]
  We propose a new type of network compression optimization technique, fully randomized tensor network compression (FCTN)<n>FCTN has significant advantages in correlation characterization and transpositional in algebra, and has notable achievements in multi-dimensional data processing and analysis.<n>We derive efficient algorithms with guarantees to solve the formulated models.
  arXiv  Detail & Related papers  (2026-02-13T14:56:37Z)
• Optimal Multiparameter Quantum Estimation of Magnonic Couplings in a Magnomechanical Cavity [0.0]
  We introduce an experimentally viable scheme to enhance the simultaneous estimation precision of the couplings $G_mc$ and $G_mb$.<n>We show that under certain conditions, heterodyne detection can closely approach the ultimate precision set by the QFIM.<n>This suggests that a measurement strategy based on heterodyne detection can offer an efficient and practical route for estimating the couplings.
  arXiv  Detail & Related papers  (2025-11-15T20:45:26Z)
• MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation [74.34220141721231]
  We present MPQ-DMv2, an improved textbfMixed textbfPrecision textbfQuantization framework for extremely low-bit textbfDiffusion textbfModels.
  arXiv  Detail & Related papers  (2025-07-06T08:16:50Z)
• Pushing the Limits of Large Language Model Quantization via the Linearity Theorem [71.3332971315821]
  We present a "line theoremarity" establishing a direct relationship between the layer-wise $ell$ reconstruction error and the model perplexity increase due to quantization. This insight enables two novel applications: (1) a simple data-free LLM quantization method using Hadamard rotations and MSE-optimal grids, dubbed HIGGS, and (2) an optimal solution to the problem of finding non-uniform per-layer quantization levels.
  arXiv  Detail & Related papers  (2024-11-26T15:35:44Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• A Multimodal Intermediate Fusion Network with Manifold Learning for Stress Detection [1.2430809884830318]
  This paper introduces an intermediate multimodal fusion network with manifold learning-based dimensionality reduction. We compare various dimensionality reduction techniques for different variations of unimodal and multimodal networks. We observe that the intermediate-level fusion with the Multi-Dimensional Scaling (MDS) manifold method showed promising results with an accuracy of 96.00%.
  arXiv  Detail & Related papers  (2024-03-12T21:06:19Z)
• Quantum phase transitions in one-dimensional nanostructures: a comparison between DFT and DMRG methodologies [0.0]
  We use Density Functional Theory and Density Matrix Renormalization Group to address electronic correlation effects in diverse molecular systems. For the confined chains, DFT performs better for metallic phases, while the highest deviations appear for the Mott and band-insulator phases.
  arXiv  Detail & Related papers  (2024-02-19T19:10:00Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• Validating phase-space methods with tensor networks in two-dimensional spin models with power-law interactions [0.0]
  We evaluate the dynamics of 2D power-law interacting XXZ models, implementable in a variety of state-of-the-art experimental platforms. We compute the spin squeezing as a measure of correlations in the system, and compare to semiclassical phase-space calculations utilizing the discrete truncated Wigner approximation (DTWA) We find the latter efficiently and accurately captures the scaling of entanglement with system size in these systems, despite the comparatively resource-intensive tensor network representation of the dynamics.
  arXiv  Detail & Related papers  (2023-05-26T20:15:24Z)
• Efficient Simulation of Dynamics in Two-Dimensional Quantum Spin Systems with Isometric Tensor Networks [0.0]
  We investigate the computational power of the recently introduced class of isometric tensor network states (isoTNSs) We discuss several technical details regarding the implementation of isoTNSs-based algorithms and compare different disentanglers. We compute the dynamical spin structure factor of 2D quantum spin systems for two paradigmatic models.
  arXiv  Detail & Related papers  (2021-12-15T19:00:05Z)
• Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition [67.95996816744251]
  State-of-the-art language models (LMs) represented by long-short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming increasingly complex and expensive for practical applications. Current quantization methods are based on uniform precision and fail to account for the varying performance sensitivity at different parts of LMs to quantization errors. Novel mixed precision neural network LM quantization methods are proposed in this paper.
  arXiv  Detail & Related papers  (2021-11-29T12:24:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.