Detecting Initial System-Environment Correlations in Open Systems
- URL: http://arxiv.org/abs/2104.01695v1
- Date: Sun, 4 Apr 2021 21:13:35 GMT
- Title: Detecting Initial System-Environment Correlations in Open Systems
- Authors: Sarah Hagen, Mark Byrd
- Abstract summary: Correlations between a system and its environment lead to errors in an open quantum system.
We show that we can detect correlations by only measuring the system itself.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Correlations between a system and its environment lead to errors in an open
quantum system. Detecting those correlations would be valuable for avoiding
and/or correcting those errors. Here we show that we can detect correlations by
only measuring the system itself if we know the cause of the interaction
between the two, for example in the case of a dipole-dipole interaction. We
investigate the unitary $U$ which is associated with the exchange Hamiltonian
and examine the ability to detect initial correlations between a system and its
environment for various types of initial states. The states we select are
motivated by realistic experimental conditions and we provide bounds for when
we can state with certainty that there are initial system-environment
correlations given experimental data.
Related papers
- Initial correlations in open quantum systems are always detectable [0.0]
We show that initial correlations between the system and the environment are always detectable.
We also find the condition for existence of the optimal unitary evolution, for which the entire correlation is locally detectable.
On the other hand, we see that one can find cases for which initial correlations between the system and the environment always remain undetectable.
arXiv Detail & Related papers (2023-11-07T10:04:40Z) - Correlated noise enhances coherence and fidelity in coupled qubits [5.787049285733455]
Noise correlation can enhance the fidelity and purity of a maximally entangled (Bell) state.
These observations may be useful in the design of high-fidelity quantum gates and communication protocols.
arXiv Detail & Related papers (2023-08-01T21:13:35Z) - Non-classicality of squeezed non-Markovian processes [0.0]
We study nonclassical effects in the dynamics of an open quantum system.
The squeezed reservoirs coupled to the system through single and two quanta exchange processes are put in the spotlight.
arXiv Detail & Related papers (2023-05-16T09:56:53Z) - Interactive System-wise Anomaly Detection [66.3766756452743]
Anomaly detection plays a fundamental role in various applications.
It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data.
We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
arXiv Detail & Related papers (2023-04-21T02:20:24Z) - Evolution of many-body systems under ancilla quantum measurements [58.720142291102135]
We study the concept of implementing quantum measurements by coupling a many-body lattice system to an ancillary degree of freedom.
We find evidence of a disentangling-entangling measurement-induced transition as was previously observed in more abstract models.
arXiv Detail & Related papers (2023-03-13T13:06:40Z) - Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations [114.17826109037048]
Ordinary Differential Equations (ODEs) have recently gained a lot of attention in machine learning.
theoretical aspects, e.g., identifiability and properties of statistical estimation are still obscure.
This paper derives a sufficient condition for the identifiability of homogeneous linear ODE systems from a sequence of equally-spaced error-free observations sampled from a single trajectory.
arXiv Detail & Related papers (2022-10-12T06:46:38Z) - Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data.
We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism.
We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
arXiv Detail & Related papers (2022-06-30T06:00:13Z) - Bridging the gap between topological non-Hermitian physics and open
quantum systems [62.997667081978825]
We show how to detect a transition between different topological phases by measuring the response to local perturbations.
Our formalism is exemplified in a 1D Hatano-Nelson model, highlighting the difference between the bosonic and fermionic cases.
arXiv Detail & Related papers (2021-09-22T18:00:17Z) - To do or not to do: finding causal relations in smart homes [2.064612766965483]
This paper introduces a new way to learn causal models from a mixture of experiments on the environment and observational data.
The core of our method is the use of selected interventions, especially our learning takes into account the variables where it is impossible to intervene.
We use our method on a smart home simulation, a use case where knowing causal relations pave the way towards explainable systems.
arXiv Detail & Related papers (2021-05-20T22:36:04Z) - Preparation of quantum correlations assisted by a steering Maxwell demon [0.0]
We study the preparations of quantum correlations from a system qubit and an auxiliary qubit.
The demon can affect the postmeasured states of system by choosing different measurements.
We present the optimal protocols for creating mutual information, entanglement, and Bell-nonlocality.
arXiv Detail & Related papers (2021-03-04T16:38:56Z) - Disentangling Observed Causal Effects from Latent Confounders using
Method of Moments [67.27068846108047]
We provide guarantees on identifiability and learnability under mild assumptions.
We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions.
arXiv Detail & Related papers (2021-01-17T07:48:45Z)
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.