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Quantum Gravity Field-Based Probability

Jincheng Zhang

Published Sep 5, 2026• 0 citations
Publisher

Abstract

Quantum Gravity Field-Based Probability explores the potential of utilizing quantum entanglement to refine probability calculations within complex quantum systems. The core claim posits the development of a novel framework – the 'Quantum Entanglement Probability Field' – that quantifies the probability of event outcomes by encoding these probabilities within a dynamically evolving field. This represents a departure from classical probability models, offering a potential pathway to improved prediction and a deeper understanding of quantum phenomena. The paper introduces the concept, details the design of the entanglement probability field, and outlines the theoretical and potential experimental implications of this research.

DOI
10.5281/zenodo.22331605

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Quantum Gravity Field-Based Probability explores the potential of utilizing quantum entanglement to refine probability calculations within complex quantum systems. The core claim posits the development of a novel framework – the 'Quantum Entanglement Probability Field' – that quantifies the probability of event outcomes by encoding these probabilities within a dynamically evolving field. This represents a departure from classical probability models, offering a potential pathway to improved prediction and a deeper understanding of quantum phenomena. The paper introduces the concept, details the design of the entanglement probability field, and outlines the theoretical and potential experimental implications of this research.
This paper introduces a novel dynamic probability field theory designed to analyze quantum entanglement properties and predict its evolution. We propose a framework that treats entanglement as a dynamically evolving probability field, incorporating self-adaptive properties to enhance quantum computation's efficiency and reliability. The core mechanism involves representing entanglement as a continuously evolving probability field, allowing for a more nuanced understanding and control over its behavior. The paper details the theoretical foundation and initial experimental explorations of this approach, highlighting its potential for improved entanglement analysis and quantum algorithm design. The primary goal is to provide a robust mathematical model for understanding and predicting entanglement dynamics.
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Quantum entanglement remains a cornerstone of modern physics, exhibiting phenomena defying classical intuition. However, its full implications, particularly concerning its non-locality, have been a persistent challenge. This paper explores a novel approach to quantum entanglement analysis, leveraging the power of probabilistic field theory. We propose a framework that dynamically models the measurement outcome of entangled particles, incorporating a 'dynamic probability field' to enhance the capture of non-local correlations. This method aims to provide a more rigorous and potentially more accurate theoretical foundation for quantum computation and communication, moving beyond traditional classical models. The core mechanism centers around treating quantum entanglement as a probabilistic system, where the correlation is inherently encoded in the evolution of the system's probability distribution. This investigation seeks to refine our understanding of entanglement's non-classical behavior through this probabilistic perspective.
This paper presents a novel approach to calibrating quantum probability fields, utilizing entanglement to achieve precise control and prediction of probabilistic outcomes. The core mechanism centers around a dynamically evolving entanglement network designed to measure and refine the probability distribution of a system, capitalizing on the inherent correlations of entangled particles. This system offers a pathway to quantum-enhanced prediction, surpassing conventional measurement techniques by directly manipulating probability distributions. The resulting framework provides a foundation for advanced quantum simulations and control, potentially revolutionizing fields reliant on probabilistic outcomes.
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