Bell and EPR experiments with signalling data
- URL: http://arxiv.org/abs/2602.05507v1
- Date: Thu, 05 Feb 2026 10:07:09 GMT
- Title: Bell and EPR experiments with signalling data
- Authors: Lucas Maquedano, Sophie Egelhaaf, Amro Abou-Hachem, Jef Pauwels, Armin Tavakoli, Ana C. S. Costa, Roope Uola,
- Abstract summary: No-signalling principle is a fundamental assumption in Bell-inequality and quantum-steering experiments.<n>We propose extensions of local hidden variable and local hidden state theories that allow for bounded, operationally quantifiable amounts of signalling.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The no-signalling principle is a fundamental assumption in Bell-inequality and quantum-steering experiments. Nonetheless, experimental imperfections can lead to apparent violations beyond those expected from finite-sample statistics. Here, we propose extensions of local hidden variable and local hidden state theories that allow for bounded, operationally quantifiable, amounts of signalling. We show how non-classicality tests can be developed for these models, both through exact methods based on the full set of observed statistics and through corrections to the standard Bell and steering inequalities. We demonstrate the applicability of these methods via two scenarios that feature apparent signalling: an IBM quantum processor and post-selected data from inefficient detectors.
Related papers
- From Calibration to Refinement: Seeking Certainty via Probabilistic Evidence Propagation for Noisy-Label Person Re-Identification [40.73759251488672]
Existing noise-robust person Re-ID methods rely on loss-correction or sample-selection strategies using softmax outputs.<n>We propose the CAlibration-to-REfinement (CARE) method, a two-stage framework that seeks certainty through probabilistic evidence propagation from calibration to refinement.<n>In the refinement stage, we design the evidence propagation refinement (EPR) that can more accurately distinguish between clean and noisy samples.
arXiv Detail & Related papers (2026-02-26T15:50:15Z) - Combating Noisy Labels through Fostering Self- and Neighbor-Consistency [120.4394402099635]
Label noise is pervasive in various real-world scenarios, posing challenges in supervised deep learning.<n>We propose a noise-robust method named Jo-SNC (textbfJoint sample selection and model regularization based on textbfSelf- and textbfNeighbor-textbfConsistency)<n>We design a self-adaptive, data-driven thresholding scheme to adjust per-class selection thresholds.
arXiv Detail & Related papers (2026-01-19T07:55:29Z) - No-signalling-projection-invariant Bell inequalities [0.0]
We show how any Bell inequality for a configuration involving $n$ parties each performing one of $m$ binary-outcome measurements has a canonical form that is no-signalling-projection invariant.<n>Specifically, the $L2$-projection of weakly signalling data onto the no-signalling polytope leaves the violation of this canonical Bell inequality unchanged.
arXiv Detail & Related papers (2025-11-10T02:04:07Z) - Almost device-independent calibration beyond Born's rule: Bell tests for cross-talk detection [0.0]
Device-independent protocols offer a new approach to information processing tasks.<n>We demonstrate how to test premises in an (almost) device-independent setting.<n>For IBM's quantum computing cloud services, we implement the prediction-based ratio protocol.
arXiv Detail & Related papers (2025-03-24T17:59:58Z) - Prediction-Powered Causal Inferences [59.98498488132307]
We focus on Prediction-Powered Causal Inferences (PPCI)<n>We first show that conditional calibration guarantees valid PPCI at population level.<n>We then introduce a sufficient representation constraint transferring validity across experiments.
arXiv Detail & Related papers (2025-02-10T10:52:17Z) - Bayesian Quantum Amplitude Estimation [46.03321798937855]
We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation.<n>In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically characterize it and self-adapt.<n>We propose a benchmark for amplitude estimation algorithms and use it to test BAE against other approaches.
arXiv Detail & Related papers (2024-12-05T18:09:41Z) - Certifiably Robust Encoding Schemes [40.54768963869454]
Quantum machine learning uses principles from quantum mechanics to process data, offering potential advances in speed and performance.
Previous work has shown that these models are susceptible to attacks that manipulate input data or exploit noise in quantum circuits.
We extend this line of research by investigating the robustness against perturbations in the classical data for a general class of data encoding schemes.
arXiv Detail & Related papers (2024-08-02T11:29:21Z) - Gaussian boson sampling validation via detector binning [0.0]
We propose binned-detector probability distributions as a suitable quantity to statistically validate GBS experiments.
We show how to compute such distributions by leveraging their connection with their respective characteristic function.
We also illustrate how binned-detector probability distributions behave when Haar-averaged over all possible interferometric networks.
arXiv Detail & Related papers (2023-10-27T12:55:52Z) - Correcting for finite statistics effects in a quantum steering experiment [33.013102271622614]
We introduce a one-sided device-independent protocol that corrects for apparent signaling effects in experimental probability distributions.<n>Our results show a significantly higher probability of violation than existing state-of-the-art inequalities.<n>This work demonstrates the power of semidefinite programming for entanglement verification and brings quantum networks closer to practical applications.
arXiv Detail & Related papers (2023-05-23T14:39:08Z) - Open-Set Likelihood Maximization for Few-Shot Learning [36.97433312193586]
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples.
We explore the popular transductive setting, which leverages the unlabelled query instances at inference.
Motivated by the observation that existing transductive methods perform poorly in open-set scenarios, we propose a generalization of the maximum likelihood principle.
arXiv Detail & Related papers (2023-01-20T01:56:19Z) - Ab-initio experimental violation of Bell inequalities [0.9786690381850356]
violation of a Bell inequality is the paradigmatic example of device-independent quantum information.
In practice, all Bell experiments rely on the precise understanding of the underlying physical mechanisms.
We propose and experimentally implement a solution to this ab-initio task.
Treating preparation and measurement devices as black-boxes, and relying on the observed statistics only, our adaptive protocol approaches the optimal Bell inequality violation.
arXiv Detail & Related papers (2021-08-02T00:39:52Z) - Foreseeing the Benefits of Incidental Supervision [83.08441990812636]
This paper studies whether we can, in a single framework, quantify the benefits of various types of incidental signals for a given target task without going through experiments.
We propose a unified PAC-Bayesian motivated informativeness measure, PABI, that characterizes the uncertainty reduction provided by incidental supervision signals.
arXiv Detail & Related papers (2020-06-09T20:59:42Z) - Bell non-locality using tensor networks and sparse recovery [0.0]
Bell's theorem, stating that quantum predictions are incompatible with a local hidden variable description, is a cornerstone of quantum theory.
We propose to analyse a Bell scenario as a tensor network, a perspective permitting to test and quantify non-locality.
It allows to prove that non-signalling correlations can be described by hidden variable models governed by a quasi-probability.
arXiv Detail & Related papers (2020-01-30T16:59:18Z)
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.