RC-HEOM Hybrid Method for Non-Perturbative Open System Dynamics

• URL: http://arxiv.org/abs/2603.22833v1
• Date: Tue, 24 Mar 2026 06:11:12 GMT
• Title: RC-HEOM Hybrid Method for Non-Perturbative Open System Dynamics
• Abstract summary: We introduce RC-HEOM, a hybrid method that unifies the strengths of both approaches.<n> RC-HEOM simultaneously retains exact non-Markovian memory and access to the RC mode.<n>These results demonstrate that RC-HEOM is a promising method for characterizing open quantum systems.
• Score: 0.33230319983854356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Hierarchical equations of motion (HEOM) method is an important non-perturbative technique, allowing numerically exact treatment of open quantum systems with strong coupling and non-Markovian memory. However, its encoding of bath memory into auxiliary density operators often limits direct access to detailed bath information. In contrast, the reaction-coordinate (RC) mapping allows direct and transparent access to the dominant collective bath mode, but its perturbative and often Markovian treatment of the residual bath restricts its reliability. In this work, we introduce RC-HEOM, a hybrid method that unifies the strengths of both approaches by combining RC mapping with a fully non-perturbative HEOM description of the residual bath. RC-HEOM simultaneously retains exact non-Markovian memory and access to the RC mode, which enables analysis of system-RC information. Applying this method to the Anderson impurity models, we directly track the emergence of the Kondo singlet from the growth of the Kondo resonance and uncover a nontrivial RC-mediated coherence revival. These results demonstrate that RC-HEOM is a promising method for characterizing open quantum systems in regimes that are difficult to access with conventional master-equation methods.

Related papers

• JSCGC: Joint Source-Channel-Generation Coding for Wireless Generative Communications [58.67925548779465]
  Joint Source-Channel-Generation Coding (JSCGC) is a generative communication paradigm that replaces the conventional decoder with a generative model at the receiver.<n>JSCGC consistently improves feature-based, semantic-level, and inferenceal quality across diverse channel conditions.
  arXiv  Detail & Related papers  (2026-06-11T03:42:03Z)
• Comparison between explicit and implicit discretization strategies for a dissipative thermal environment [0.0]
  We compare explicit wave function-based discretization [via multi-layer multi-configuration time-dependent Hartree (ML-MCTDH)] and the implicit density matrix-based master equation method [via tree tensor network hierarchical equations of motion (TTN-HEOM)]<n>For dissipative baths characterized by exponentially decaying bath correlation functions, the implicit discretization approach of HEOM proves significantly more efficient than explicit discretization of the bath into discrete harmonic modes.
  arXiv  Detail & Related papers  (2026-01-19T14:41:19Z)
• Joint Source-Channel-Generation Coding: From Distortion-oriented Reconstruction to Semantic-consistent Generation [58.67925548779465]
  We propose Joint Source-Channel-Generation Coding (JSCGC), a novel paradigm that shifts the focus from perceptual reconstruction to probabilistic generation.<n>JSCGC improves substantially semantic quality and semantic fidelity, significantly outperforming conventional distortion-oriented J SCC methods.
  arXiv  Detail & Related papers  (2026-01-19T08:12:47Z)
• SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks [73.41290017870097]
  SecDiff is a plug-and-play, diffusion-aided decoding framework.<n>It significantly enhances the security and robustness of deep J SCC under adversarial wireless environments.
  arXiv  Detail & Related papers  (2025-11-03T11:24:06Z)
• Scalable Distributed Memory Implementation of the Quasi-Adiabatic Propagator Path Integral [0.0]
  We present a scalable distributed memory implementation of the Mask Assisted Coarse Graining of Influence Coefficients.<n>The method exploits the memory resources of multiple compute nodes and mitigates the memory bottleneck via a new pre-merging algorithm.<n>The efficiency of the new implementation is demonstrated in large-scale dissipative quantum dynamics simulations.
  arXiv  Detail & Related papers  (2025-06-03T17:56:18Z)
• Numerically efficient quasi-adiabatic propagator path integral approach with two independent non-commuting baths [0.0]
  We address the numerical efficiency and accuracy of approximations that have been introduced for the QUAPI method with a single general environment.<n>Specifically, we consider a sharply defined cut-off of the memory time, path filtering and mask assisted coarse graining of influence functional coefficients as approximations.<n>Our findings demonstrate that if system coupling operators to different baths do not commute, the additive nature of the statistically independent environments may be misleading.
  arXiv  Detail & Related papers  (2025-03-27T16:58:13Z)
• Adding Additional Control to One-Step Diffusion with Joint Distribution Matching [58.37264951734603]
  JDM is a novel approach that minimizes the reverse KL divergence between image-condition joint distributions.<n>By deriving a tractable upper bound, JDM decouples fidelity learning from condition learning.<n>This asymmetric distillation scheme enables our one-step student to handle controls unknown to the teacher model.
  arXiv  Detail & Related papers  (2025-03-09T15:06:50Z)
• Rectified Diffusion Guidance for Conditional Generation [94.83538269086613]
  We revisit the theory behind CFG and rigorously confirm that the improper combination coefficients (textiti.e.) brings about expectation shift the generative distribution.<n>We show that our approach enjoys a textbftextitform solution given the strength.<n> Empirical evidence on real-world data demonstrate the compatibility of our design with existing state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2024-10-24T13:41:32Z)
• On stability issues of the HEOM method [0.0]
  We show that the necessary truncation of the hierarchy of auxiliary density operators seems to introduce errors which are not fully controllable. We find that truncating the hierarchy to any finite size can be problematic for strong coupling to a dissipative reservoir.
  arXiv  Detail & Related papers  (2024-08-10T11:13:18Z)
• Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic [61.968469104271676]
  We propose an RL methodology attuned to the mixing time by employing a multi-level Monte Carlo estimator for the critic, the actor, and the average reward embedded within an actor-critic (AC) algorithm. We experimentally show that these alleviated restrictions on the technical conditions required for stability translate to superior performance in practice for RL problems with sparse rewards.
  arXiv  Detail & Related papers  (2023-01-28T04:12:56Z)
• Capturing non-Markovian dynamics with the reaction coordinate method [0.0]
  We study the pure decoherence model of a spin coupled to a harmonic reservoir. We find that the RC method can quantitatively capture non-Markovian effects at strong system-bath coupling. Lastly, we apply our RC method and study the spin-boson model in the non-Markovian regime.
  arXiv  Detail & Related papers  (2021-10-06T02:02:20Z)
• Scalable Bayesian Inverse Reinforcement Learning [93.27920030279586]
  We introduce Approximate Variational Reward Imitation Learning (AVRIL) Our method addresses the ill-posed nature of the inverse reinforcement learning problem. Applying our method to real medical data alongside classic control simulations, we demonstrate Bayesian reward inference in environments beyond the scope of current methods.
  arXiv  Detail & Related papers  (2021-02-12T12:32:02Z)

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.