Correlations, information backflow, and objectivity in a class of pure dephasing models

• URL: http://arxiv.org/abs/2201.10573v1
• Date: Tue, 25 Jan 2022 19:00:06 GMT
• Title: Correlations, information backflow, and objectivity in a class of pure dephasing models
• Authors: Nina Megier, Andrea Smirne, Steve Campbell, Bassano Vacchini
• Abstract summary: We critically examine the role that correlations established between a system and fragments of its environment play in characterising the ensuing dynamics. We employ a class of dephasing models where the state of the initial environment represents a tunable degree of freedom that qualitatively and quantitatively affects the correlation profiles. We demonstrate that for precisely the same non-Markovian reduced dynamics of the system arising from different microscopic models, some will exhibit quantum Darwinistic features, while others show no meaningful notion of classical objectivity is present.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We critically examine the role that correlations established between a system and fragments of its environment play in characterising the ensuing dynamics. We employ a class of dephasing models where the state of the initial environment represents a tunable degree of freedom that qualitatively and quantitatively affects the correlation profiles, but nevertheless results in the same reduced dynamics for the system. We apply recently developed tools for the characterisation of non-Markovianity to carefully assess the role that correlations, as quantified by the (quantum) Jensen-Shannon divergence and relative entropy, as well as changes in the environmental state, play in whether the conditions for classical objectivity within the quantum Darwinism paradigm are met. We demonstrate that for precisely the same non-Markovian reduced dynamics of the system arising from different microscopic models, some will exhibit quantum Darwinistic features, while others show no meaningful notion of classical objectivity is present. Furthermore, our results highlight that the non-Markovian nature of an environment does not a priori prevent a system from redundantly proliferating relevant information, but rather it is the system's ability to establish the requisite correlations that is the crucial factor in the manifestation of classical objectivity.

Related papers

• Unified Causality Analysis Based on the Degrees of Freedom [1.2289361708127877]
  This paper presents a unified method capable of identifying fundamental causal relationships between pairs of systems. By analyzing the degrees of freedom in the system, our approach provides a more comprehensive understanding of both causal influence and hidden confounders. This unified framework is validated through theoretical models and simulations, demonstrating its robustness and potential for broader application.
  arXiv  Detail & Related papers  (2024-10-25T10:57:35Z)
• Systems with Switching Causal Relations: A Meta-Causal Perspective [18.752058058199847]
  flexibility of agents' actions or tipping points in the environmental process can change the qualitative dynamics of the system. New causal relationships may emerge, while existing ones change or disappear, resulting in an altered causal graph. We propose the concept of meta-causal states, which groups classical causal models into clusters based on equivalent qualitative behavior.
  arXiv  Detail & Related papers  (2024-10-16T21:32:31Z)
• Sequential Representation Learning via Static-Dynamic Conditional Disentanglement [58.19137637859017]
  This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-independent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between those factors by explicitly accounting for the causal relationship between the static/dynamic variables. Experiments show that the proposed approach outperforms previous complex state-of-the-art techniques in scenarios where the dynamics of a scene are influenced by its content.
  arXiv  Detail & Related papers  (2024-08-10T17:04:39Z)
• Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies [58.179981892921056]
  This work introduces a novel principle for disentanglement we call mechanism sparsity regularization. We propose a representation learning method that induces disentanglement by simultaneously learning the latent factors. We show that the latent factors can be recovered by regularizing the learned causal graph to be sparse.
  arXiv  Detail & Related papers  (2024-01-10T02:38:21Z)
• High-dimensional monitoring and the emergence of realism via multiple observers [41.94295877935867]
  Correlation is the basic mechanism of every measurement model. We introduce a model that interpolates between weak and strong non-selective measurements for qudits.
  arXiv  Detail & Related papers  (2023-05-13T13:42:19Z)
• Identifying Weight-Variant Latent Causal Models [82.14087963690561]
  We find that transitivity acts as a key role in impeding the identifiability of latent causal representations. Under some mild assumptions, we can show that the latent causal representations can be identified up to trivial permutation and scaling. We propose a novel method, termed Structural caUsAl Variational autoEncoder, which directly learns latent causal representations and causal relationships among them.
  arXiv  Detail & Related papers  (2022-08-30T11:12:59Z)
• Entropy production in non-Markovian collision models: Information backflow vs system-environment correlations [0.0]
  We investigate the irreversible entropy production of a qubit in contact with an environment modelled by a microscopic collision model. Our results suggest that the mechanism underpinning the negative entropy production rates is not solely non-Markovianity through information backflow.
  arXiv  Detail & Related papers  (2022-04-20T14:59:09Z)
• Quantum non-Markovian "casual bystander" environments [0.0]
  Quantum memory effects can be induced even when the degrees of freedom associated to the environment are not affected at all during the system evolution. General properties of the resulting dynamics are studied with focus on the system-environment correlations, a collisional measurement-based representation, and the quantum regression hypothesis.
  arXiv  Detail & Related papers  (2021-09-20T14:24:18Z)
• Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA [81.4991350761909]
  Independent component analysis (ICA) refers to an ensemble of methods which formalize this goal and provide estimation procedure for practical application. We show that the latent variables can be recovered up to a permutation if one regularizes the latent mechanisms to be sparse.
  arXiv  Detail & Related papers  (2021-07-21T14:22:14Z)
• BayesIMP: Uncertainty Quantification for Causal Data Fusion [52.184885680729224]
  We study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable. We introduce a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space.
  arXiv  Detail & Related papers  (2021-06-07T10:14:18Z)
• Latent Causal Invariant Model [128.7508609492542]
  Current supervised learning can learn spurious correlation during the data-fitting process. We propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.
  arXiv  Detail & Related papers  (2020-11-04T10:00:27Z)

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.