Separability, Contextuality, and the Quantum Frame Problem

• URL: http://arxiv.org/abs/2304.10010v1
• Date: Wed, 19 Apr 2023 23:32:19 GMT
• Title: Separability, Contextuality, and the Quantum Frame Problem
• Authors: Chris Fields and James F. Glazebrook
• Abstract summary: We study the relationship between assumptions of state separability and both preparation and measurement contextuality. We show how contextuality is generically induced in state preparation and measurement by basis choice, thermodynamic exchange, and the imposition of a priori causal models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the relationship between assumptions of state separability and both preparation and measurement contextuality, and the relationship of both of these to the frame problem, the problem of predicting what does not change in consequence of an action. We state a quantum analog of the latter and prove its undecidability. We show how contextuality is generically induced in state preparation and measurement by basis choice, thermodynamic exchange, and the imposition of a priori causal models, and how fine-tuning assumptions appear ubiquitously in settings characterized as non-contextual.

Related papers

• 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)
• Corrected Bell and Noncontextuality Inequalities for Realistic Experiments [1.099532646524593]
  Contextuality is a feature of quantum correlations. It is crucial from a foundational perspective as a nonclassical phenomenon, and from an applied perspective as a resource for quantum advantage. We prove the continuity of a known measure of contextuality, the contextual fraction, which ensures its robustness to noise. We then bound the extent to which these relaxations can account for contextuality, culminating in a notion of genuine contextuality, which is robust to experimental imperfections.
  arXiv  Detail & Related papers  (2023-10-30T09:43:39Z)
• Thermodynamic precision in the nonequilibrium exchange scenario [0.0]
  We investigate the use of entangled nonequilibrium steady states as end-points of thermodynamic cycles. We shed light on the role of the entanglement between the two qubits for precise work absorption.
  arXiv  Detail & Related papers  (2023-07-25T08:54:46Z)
• A Measure-Theoretic Axiomatisation of Causality [55.6970314129444]
  We argue in favour of taking Kolmogorov's measure-theoretic axiomatisation of probability as the starting point towards an axiomatisation of causality. Our proposed framework is rigorously grounded in measure theory, but it also sheds light on long-standing limitations of existing frameworks.
  arXiv  Detail & Related papers  (2023-05-19T13:15:48Z)
• Contextuality as a Precondition for Quantum Entanglement [0.0]
  We prove that entanglement in a nonlocal scenario can arise only if there is preparation and measurement contextuality. As a direct consequence, our result allows us to translate any inequality for testing preparation and measurement contextuality into an entanglement test.
  arXiv  Detail & Related papers  (2022-09-20T18:36:34Z)
• Incompatibility in restricted operational theories: connecting contextuality and steering [0.0]
  We show that for a class of bipartite states the steerability of the state by given set of measurements is equivalent to non-existence of preparation noncontextual hidden variable model. The connection between steering and contextuality is provided by the concept of incompatibility in restricted theories.
  arXiv  Detail & Related papers  (2021-12-16T10:53:48Z)
• 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)
• 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)
• Contextuality-by-default for behaviours in compatibility scenarios [0.0]
  We show that the assumption that a physical measurement has to be understood as a contextual collection of random variables is implicit in the compatibility-hypergraph approach to contextuality (CA) We introduce in CA the non-degeneracy condition, which is the analogous of consistent connectedness, and prove that this condition is, in general, weaker than non-disturbance condition. We introduce the idea of extended contextuality for behaviours and prove that a behaviour is non-contextual in the standard sense iff it is non-degenerate and non-contextual in the extended sense.
  arXiv  Detail & Related papers  (2020-08-05T17:54:14Z)
• The empirical duality gap of constrained statistical learning [115.23598260228587]
  We study the study of constrained statistical learning problems, the unconstrained version of which are at the core of virtually all modern information processing. We propose to tackle the constrained statistical problem overcoming its infinite dimensionality, unknown distributions, and constraints by leveraging finite dimensional parameterizations, sample averages, and duality theory. We demonstrate the effectiveness and usefulness of this constrained formulation in a fair learning application.
  arXiv  Detail & Related papers  (2020-02-12T19:12:29Z)

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.