Linear Algebra of Generalized Contextuality in All Prepare-Transform-Measure Scenarios
- URL: http://arxiv.org/abs/2607.26139v1
- Date: Tue, 28 Jul 2026 18:00:03 GMT
- Title: Linear Algebra of Generalized Contextuality in All Prepare-Transform-Measure Scenarios
- Abstract summary: Generalized contextuality is a canonical distinguishing property of non generalized probabilistic theories.<n>We develop a statistics-first linear-algebraic framework for contextuality in prepare-measure scenarios.<n>Our findings shed new light on the significant role of compositional structures in the phenomenon of generalized contextuality.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generalized contextuality is a canonical distinguishing property of nonclassical generalized probabilistic theories, in particular quantum mechanics. Methods for certification and characterization of generalized contextuality of a given generalized probabilistic theory are well developed for prepare-measure and single-stage prepare-transform-measure scenarios. In a recent work [arXiv:2512.10000], a bottom-up, statistics-first linear-algebraic framework for contextuality in prepare-measure scenarios was introduced. We extend this approach to operational scenarios with sequential transformations with an arbitrary number of stages. We give a full decision procedure for contextuality of such scenarios within operational theories and analyze its computational complexity. In particular, our decision procedure has a complexity linearly exponential in the minimum generalized probabilistic theory (GPT) dimension, and polynomial in the number of procedures. We demonstrate our framework and approach through multiple examples, including Spekkens' toy theory and the 8-state single-qubit stabilizer theory. In particular, we construct an operational theory in which contextuality manifests itself only in the sequential structure of the transformations. Our findings thus shed new light on the significant role of compositional structures in the phenomenon of generalized contextuality.
Related papers
- Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks [50.79340930913805]
We study a class of inductive tasks that unifies several synthetic tasks known in the literature, including in-context n-grams and multi-hop reasoning.<n>By casting circuit formation as a low-dimensional dynamical phenomenon, we take a step toward a predictive theory of how Transformers learn.
arXiv Detail & Related papers (2026-07-13T17:56:11Z) - In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning [51.56484100374058]
We introduce a principled risk decomposition that separates the total ICL risk into two components: Bayes Gap and Posterior Variance.<n>For a uniform-attention Transformer, we derive a non-asymptotic upper bound on this gap, which explicitly clarifies the dependence on the number of pretraining prompts.<n>The Posterior Variance is a model-independent risk representing the intrinsic task uncertainty.
arXiv Detail & Related papers (2025-10-13T03:42:31Z) - Simplicial methods in the resource theory of contextuality [0.0]
Building on the theory of simplicial distributions, we introduce event scenarios as a functorial generalization of presheaf-theoretic measurement scenarios.<n>We define symmetric monoidal structures on these categories and extend the distribution functor to a setting, yielding a resource theory that generalizes the presheaf-theoretic notion of simulations.
arXiv Detail & Related papers (2025-05-29T21:14:55Z) - Generalised Process Theories [0.0]
We propose an alternative formalization using operad algebras, motivated by recent results connecting SMCs to operadic structures.<n>We provide an accessible yet rigorous formulation that unifies and extends traditional process-theoretic approaches.
arXiv Detail & Related papers (2025-02-14T18:47:07Z) - Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems [53.03951222945921]
We analyze smoothed (perturbed) policies, adding controlled random perturbations to the direction used by the linear oracle.<n>Our main contribution is a generalization bound that decomposes the excess risk into perturbation bias, statistical estimation error, and optimization error.<n>We illustrate the scope of the results on applications such as vehicle scheduling, highlighting how smoothing enables both tractable training and controlled generalization.
arXiv Detail & Related papers (2024-07-24T12:00:30Z) - Real quantum operations and state transformations [44.99833362998488]
Resource theory of imaginarity provides a useful framework to understand the role of complex numbers.
In the first part of this article, we study the properties of real'' (quantum) operations in single-party and bipartite settings.
In the second part of this article, we focus on the problem of single copy state transformation via real quantum operations.
arXiv Detail & Related papers (2022-10-28T01:08:16Z) - Fractal Structure and Generalization Properties of Stochastic
Optimization Algorithms [71.62575565990502]
We prove that the generalization error of an optimization algorithm can be bounded on the complexity' of the fractal structure that underlies its generalization measure.
We further specialize our results to specific problems (e.g., linear/logistic regression, one hidden/layered neural networks) and algorithms.
arXiv Detail & Related papers (2021-06-09T08:05:36Z) - Using a resource theoretic perspective to witness and engineer quantum
generalized contextuality for prepare-and-measure scenarios [0.0]
We employ the resource theory of generalized contextuality as a tool for analyzing the structure of prepare-and-measure scenarios.
We argue that this framework simplifies proofs of quantum contextuality in complex scenarios.
arXiv Detail & Related papers (2021-02-20T23:06:03Z) - Contextuality scenarios arising from networks of stochastic processes [68.8204255655161]
An empirical model is said contextual if its distributions cannot be obtained marginalizing a joint distribution over X.
We present a different and classical source of contextual empirical models: the interaction among many processes.
The statistical behavior of the network in the long run makes the empirical model generically contextual and even strongly contextual.
arXiv Detail & Related papers (2020-06-22T16:57:52Z) - A structure theorem for generalized-noncontextual ontological models [0.0]
We use a process-theoretic framework to prove that every generalized-noncontextual ontological model of a tomographically local operational theory has a surprisingly rigid and simple mathematical structure.
We extend known results concerning the equivalence of different notions of classicality from prepare-measure scenarios to arbitrary compositional scenarios.
arXiv Detail & Related papers (2020-05-14T17:28:19Z) - Solvable Criterion for the Contextuality of any Prepare-and-Measure
Scenario [0.0]
An operationally noncontextual ontological model of the quantum statistics associated with the prepare-and-measure scenario is constructed.
A mathematical criterion, called unit separability, is formulated as the relevant classicality criterion.
We reformulate our results in the framework of generalized probabilistic theories.
arXiv Detail & Related papers (2020-03-13T18:00:05Z) - A General Framework for Consistent Structured Prediction with Implicit
Loss Embeddings [113.15416137912399]
We propose and analyze a novel theoretical and algorithmic framework for structured prediction.
We study a large class of loss functions that implicitly defines a suitable geometry on the problem.
When dealing with output spaces with infinite cardinality, a suitable implicit formulation of the estimator is shown to be crucial.
arXiv Detail & Related papers (2020-02-13T10:30:04Z)
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.