Experimentally friendly approach towards nonlocal correlations in
multisetting N -partite Bell scenarios
- URL: http://arxiv.org/abs/2009.11691v2
- Date: Thu, 1 Apr 2021 17:59:58 GMT
- Title: Experimentally friendly approach towards nonlocal correlations in
multisetting N -partite Bell scenarios
- Authors: Artur Barasi\'nski, Anton\'in \v{C}ernoch, Wies{\l}aw Laskowski, Karel
Lemr, Tam\'as V\'ertesi, and Jan Soubusta
- Abstract summary: We study a recently proposed operational measure of nonlocality by Fonseca and Parisio[Phys. A 92, 03(R)] which describes the probability of violation of local realism under randomly sampled observables.
We show that even with both a randomly chosen $N$-qubit pure state and randomly chosen measurement bases, a violation of local realism can be detected experimentally almost $100%$ of the time.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work, we study a recently proposed operational measure of nonlocality
by Fonseca and Parisio~[Phys. Rev. A 92, 030101(R) (2015)] which describes the
probability of violation of local realism under randomly sampled observables,
and the strength of such violation as described by resistance to white noise
admixture. While our knowledge concerning these quantities is well established
from a theoretical point of view, the experimental counterpart is a
considerably harder task and very little has been done in this field. It is
caused by the lack of complete knowledge about the facets of the local polytope
required for the analysis. In this paper, we propose a simple procedure towards
experimentally determining both quantities for $N$-qubit pure states, based on
the incomplete set of tight Bell inequalities. We show that the imprecision
arising from this approach is of similar magnitude as the potential measurement
errors. We also show that even with both a randomly chosen $N$-qubit pure state
and randomly chosen measurement bases, a violation of local realism can be
detected experimentally almost $100\%$ of the time. Among other applications,
our work provides a feasible alternative for the witnessing of genuine
multipartite entanglement without aligned reference frames.
Related papers
- Strength of statistical evidence for genuine tripartite nonlocality [0.0]
Recent advancements in network nonlocality have led to the concept of local operations and shared randomness-based genuine multipartite nonlocality (LOSR-GMNL)
This paper focuses on a tripartite scenario where the goal is to exhibit correlations impossible in a network where each two-party subset shares bipartite resources and every party has access to unlimited shared randomness.
arXiv Detail & Related papers (2024-07-28T21:12:52Z) - To Believe or Not to Believe Your LLM [51.2579827761899]
We explore uncertainty quantification in large language models (LLMs)
We derive an information-theoretic metric that allows to reliably detect when only epistemic uncertainty is large.
We conduct a series of experiments which demonstrate the advantage of our formulation.
arXiv Detail & Related papers (2024-06-04T17:58:18Z) - Invariant Causal Prediction with Local Models [52.161513027831646]
We consider the task of identifying the causal parents of a target variable among a set of candidates from observational data.
We introduce a practical method called L-ICP ($textbfL$ocalized $textbfI$nvariant $textbfCa$usal $textbfP$rediction), which is based on a hypothesis test for parent identification using a ratio of minimum and maximum statistics.
arXiv Detail & Related papers (2024-01-10T15:34:42Z) - A Robustness Analysis of Blind Source Separation [91.3755431537592]
Blind source separation (BSS) aims to recover an unobserved signal from its mixture $X=f(S)$ under the condition that the transformation $f$ is invertible but unknown.
We present a general framework for analysing such violations and quantifying their impact on the blind recovery of $S$ from $X$.
We show that a generic BSS-solution in response to general deviations from its defining structural assumptions can be profitably analysed in the form of explicit continuity guarantees.
arXiv Detail & Related papers (2023-03-17T16:30:51Z) - Coincidence postselection for genuine multipartite nonlocality: Causal
diagrams and threshold efficiencies [0.0]
We show how to close the detection loophole for a coincidence detection in demonstrations of nonlocality and GMN.
Our results imply that genuine $N$-partite nonlocality can be generated from $N$ independent particle sources even when allowing for non-ideal detectors.
arXiv Detail & Related papers (2022-07-27T15:21:38Z) - Optimal tests of genuine multipartite nonlocality [0.0]
We propose an optimal numerical test for genuine multipartite nonlocality based on linear programming.
We analyze to what extent the Bell scenario involving two measurement settings can be used to determine genuine $n$-way non-local correlations.
arXiv Detail & Related papers (2022-06-17T15:44:14Z) - Experimental demonstration of genuine tripartite nonlocality under
strict locality conditions [16.812051020169257]
Nonlocality captures one of the counterintuitive features of nature that defies classical intuition.
Recent investigations reveal that our physical world's nonlocality is at least tripartite.
We experimentally demonstrate such genuine tripartite nonlocality in a network under strict locality constraints.
arXiv Detail & Related papers (2022-03-02T06:08:19Z) - Violations of locality and free choice are equivalent resources in Bell
experiments [0.0]
Bell inequalities rest on three fundamental assumptions: realism, locality, and free choice.
We investigate the extent to which a given assumption needs to be relaxed for the other to hold at all costs.
Despite their disparate character, we show that both assumptions are equally costly.
arXiv Detail & Related papers (2021-05-19T10:04:38Z) - A One-step Approach to Covariate Shift Adaptation [82.01909503235385]
A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution.
We propose a novel one-step approach that jointly learns the predictive model and the associated weights in one optimization.
arXiv Detail & Related papers (2020-07-08T11:35:47Z) - Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable
Neural Distribution Alignment [52.02794488304448]
We propose a new distribution alignment method based on a log-likelihood ratio statistic and normalizing flows.
We experimentally verify that minimizing the resulting objective results in domain alignment that preserves the local structure of input domains.
arXiv Detail & Related papers (2020-03-26T22:10:04Z) - Using Randomness to decide among Locality, Realism and Ergodicity [91.3755431537592]
An experiment is proposed to find out, or at least to get an indication about, which one is false.
The results of such experiment would be important not only to the foundations of Quantum Mechanics.
arXiv Detail & Related papers (2020-01-06T19:26:32Z)
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.