Non-perfect propagation of information to noisy environment with
self-evolution
- URL: http://arxiv.org/abs/2201.11606v2
- Date: Tue, 24 May 2022 12:43:11 GMT
- Title: Non-perfect propagation of information to noisy environment with
self-evolution
- Authors: Piotr Mironowicz, Pawe{\l} Horodecki, Ryszard Horodecki
- Abstract summary: We study the non-perfect propagation of information to evolving low-dimensional environment.
We derive objectivity parameters for a model of three interacting qubits.
- Score: 0.11719282046304676
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study the non-perfect propagation of information to evolving
low-dimensional environment that includes self-evolution as well as noisy
initial states and analyze interrelations between the degree of objectivization
and environment parameters. In particular, we consider an analytical model of
three interacting qubits and derive its objectivity parameters. The numerical
analysis shows that the quality of the spectrum broadcast structure formed
during the interaction may exhibit non-monotonicity both in the speed of
self-dynamics of the environment as well as its mixedness. The former effect is
particularly strong, showing that -- considering part of the environment as a
measurement apparatus -- an increase of the external magnetic field acting on
the environment may turn the very vague measurement into close to ideal. The
above effects suggest that quantum objectivity may appear after increasing the
dynamics of the environment, although not with respect to the pointer basis,
but some other one which we call generalized pointer or indicator basis.
Furthermore, it seems also that when the objectivity is poor it may be
improved, at least by some amount, by increasing thermal noise. We provide
further evidence of that by analyzing the upper bounds on distance to the set
of states representing perfect objectivity in the case of a higher number of
qubits.
Related papers
- The role of initial system-environment correlations in the accuracies of parameters within spin-spin model [0.0]
We investigate the effect of initial system-environment correlations to improve the estimation of environment parameters.
In the temperature estimation case, our results are promising as one can improve the precision of the estimates by orders of magnitude.
In the case of coupling strength, interestingly the accuracy was found to be continuously increasing in both with and without correlations cases.
arXiv Detail & Related papers (2024-07-04T02:25:51Z) - Neural Acoustic Context Field: Rendering Realistic Room Impulse Response
With Neural Fields [61.07542274267568]
This letter proposes a novel Neural Acoustic Context Field approach, called NACF, to parameterize an audio scene.
Driven by the unique properties of RIR, we design a temporal correlation module and multi-scale energy decay criterion.
Experimental results show that NACF outperforms existing field-based methods by a notable margin.
arXiv Detail & Related papers (2023-09-27T19:50:50Z) - Open quantum system in the indefinite environment [13.979213066536394]
In this paper, we investigate the interference engineering of the open quantum system.
The environment is made indefinite either through the use of an interferometer or the introduction of auxiliary qubits.
arXiv Detail & Related papers (2023-07-13T07:52:48Z) - 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) - Correlations, information backflow, and objectivity in a class of pure
dephasing models [0.0]
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.
arXiv Detail & Related papers (2022-01-25T19:00:06Z) - Information is Power: Intrinsic Control via Information Capture [110.3143711650806]
We argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model.
This objective induces an agent to both gather information about its environment, corresponding to reducing uncertainty, and to gain control over its environment, corresponding to reducing the unpredictability of future world states.
arXiv Detail & Related papers (2021-12-07T18:50:42Z) - Visual Vibration Tomography: Estimating Interior Material Properties
from Monocular Video [66.94502090429806]
An object's interior material properties, while invisible to the human eye, determine motion observed on its surface.
We propose an approach that estimates heterogeneous material properties of an object from a monocular video of its surface vibrations.
arXiv Detail & Related papers (2021-04-06T18:05:27Z) - Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
Inferring the parameters of a model based on experimental observations is central to the scientific method.
A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations.
We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
arXiv Detail & Related papers (2021-02-12T12:23:13Z) - Causal Discovery in Physical Systems from Videos [123.79211190669821]
Causal discovery is at the core of human cognition.
We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure.
arXiv Detail & Related papers (2020-07-01T17:29:57Z) - Visual Grounding of Learned Physical Models [66.04898704928517]
Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions.
We present a neural model that simultaneously reasons about physics and makes future predictions based on visual and dynamics priors.
Experiments show that our model can infer the physical properties within a few observations, which allows the model to quickly adapt to unseen scenarios and make accurate predictions into the future.
arXiv Detail & Related papers (2020-04-28T17:06:38Z) - Quantum probing beyond pure dephasing [0.0]
We analyze the performance of a single-qubit probe in characterizing Ohmic bosonic environments at thermal equilibrium.
In particular, we analyze the effects of tuning the interaction Hamiltonian between the probe and the environment.
arXiv Detail & Related papers (2020-03-09T10:06:39Z)
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.