Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Particle Existence Probability Field (PEPF)

Jincheng Zhang

Published Aug 17, 2026• 0 citations
Publisher

Abstract

This paper proposes a novel framework for understanding particle behavior, termed the Particle Existence Probability Field (PEPF). The core concept challenges the traditional wave function interpretation by suggesting that a particle's probability of existence is intrinsically linked to a surrounding probability field. Rather than simply representing a probability density derived from a wave function, the PEPF posits a dynamic, interconnected system where the field itself governs the likelihood of a particle being found at a specific location and time. This framework is predicated on high-precision quantum experiments designed to validate its predictions. The theoretical underpinnings involve a re-evaluation of the fundamental nature of particles and their relationship to the physical universe. The PEPF offers a potential pathway to resolving longstanding paradoxes in quantum mechanics and opens avenues for new theoretical developments. The presented model aims to replace the concept of the particle as a fixed, localized entity with a more fluid, field-dependent representation.

DOI
10.5281/zenodo.21974607

Related papers

Particle Existence Probability Spectrum (PEPS)

Jincheng Zhang

This paper introduces the Particle Existence Probability Spectrum (PEPS), a novel theoretical framework that challenges the conventional understanding of wave function collapse in quantum mechanics. The core concept posits that the probability distribution of a particle's existence itself possesses a structured spectrum, denoted as *Sp(ω)*. This spectrum represents a dynamic and inherently probabilistic organization of potential locations rather than a sudden, deterministic collapse of a pre-existing wave function. The theoretical underpinnings are developed through a rigorous mathematical treatment of quantum mechanics, emphasizing the continuous nature of probability. The experimental validation of PEPS relies on high-precision interference experiments, designed to reveal the underlying structure of the probability distribution. The implications of PEPS extend to a re-evaluation of wave function interpretation and could potentially unlock new insights into the fundamental nature of reality. The central equation for the PEPS is: *Sp(ω) = ∫ |ψ(x, ω)|^2 dx* Where: * *Sp(ω)* is the Particle Existence Probability Spectrum. * *|ψ(x, ω)|^2* is the probability density of finding the particle at position *x* with frequency *ω*. * *∫* denotes the integral over all possible positions *x*.

•Aug 17, 2026•0 citations

Quantum Field Boundary Generator (QFBG)

Jincheng Zhang

This paper introduces the Quantum Field Boundary Generator (QFBG), a theoretical framework proposing that particle boundaries are not fixed physical constructs, but rather emergent properties arising from the self-organizing dynamics of quantum fields. The core concept involves utilizing a field operator, denoted as [ψ], to dynamically generate and maintain particle boundaries. This approach offers a potential explanation for the observed deterministic nature of particle behavior within the framework of quantum mechanics and possesses potential implications for high-energy physics and could be considered for Nobel Prize recognition. The QFBG avoids the need for pre-defined particle shapes or external constraints, allowing for a purely field-based description of particle existence and interaction. The mathematics presented here focuses on the fundamental equations governing the field and the boundary generation process, offering a novel perspective on particle formation and stability. This framework presents a significant departure from traditional models of particle physics, suggesting a more intrinsic and dynamically-determined nature of matter.

•Aug 17, 2026•0 citations

Quantum Entanglement-Based Probability Model

Jincheng Zhang

Quantum entanglement, a bizarre phenomenon where two or more particles become linked and share the same fate, has captivated physicists and philosophers alike. This paper explores the potential of leveraging quantum entanglement to revolutionize probability theory and, consequently, quantum computation. We propose a novel probabilistic model predicated on the inherent nature of quantum entanglement, aiming to provide a deeper understanding of probability and enhance quantum machine learning capabilities. The core of this work lies in establishing a probabilistic model intrinsically linked to the quantum entanglement mechanism, offering a fundamentally new approach to interpreting and optimizing quantum computations. The model will be designed to function as a computational foundation for quantum algorithms, addressing the limitations of traditional probabilistic methods. This paper will detail the theoretical framework, preliminary implementation, and potential applications of this new model.

•Aug 31, 2026•0 citations

Quantum Measurement Structure Field (QMSF)

Jincheng Zhang

This paper introduces the Quantum Measurement Structure Field (QMSF) hypothesis, a novel framework aimed at resolving longstanding challenges in quantum measurement theory. The core concept posits that during a quantum measurement, a structured field, denoted as [Sm(x,t)], emerges, which fundamentally determines the outcome's stability and the observed result. This framework moves beyond the traditional interpretations of wave function collapse and probabilistic outcomes, proposing a dynamic and structured reality shaped by the measurement process itself. The paper outlines the foundational principles of QMSF, highlighting its potential to address fundamental questions regarding the nature of reality, the role of the observer, and the mechanisms underlying quantum measurement. Further development and investigation of QMSF are presented as a potential pathway towards a deeper understanding of quantum mechanics and a significant advance in the field. ---

•Aug 17, 2026•0 citations

Holistic Probability Wave Function

Jincheng Zhang

This paper explores the concept of a probabilistic wave function – a fundamental representation of the observable universe – designed to model long-term evolution of complex systems through probabilistic interplay. We propose a novel approach to wave function construction, focusing on a dynamically evolving system that captures the probabilities of numerous variables. The core claim is that this approach, while requiring significant computational resources, could provide a deeper understanding of fundamental physics and allow for modeling of complex systems with unprecedented predictive capabilities. This work addresses the limitations of traditional probability models by integrating a self-modifying wave function that evolves over time, representing the probabilities of different outcomes.

•Aug 31, 2026•0 citations

Quantum Gravity Field-Based Probability

Jincheng Zhang

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.

•Sep 5, 2026•0 citations
This paper proposes a novel framework for understanding particle behavior, termed the Particle Existence Probability Field (PEPF). The core concept challenges the traditional wave function interpretation by suggesting that a particle's probability of existence is intrinsically linked to a surrounding probability field. Rather than simply representing a probability density derived from a wave function, the PEPF posits a dynamic, interconnected system where the field itself governs the likelihood of a particle being found at a specific location and time. This framework is predicated on high-precision quantum experiments designed to validate its predictions. The theoretical underpinnings involve a re-evaluation of the fundamental nature of particles and their relationship to the physical universe. The PEPF offers a potential pathway to resolving longstanding paradoxes in quantum mechanics and opens avenues for new theoretical developments. The presented model aims to replace the concept of the particle as a fixed, localized entity with a more fluid, field-dependent representation.
This paper introduces the Particle Existence Probability Spectrum (PEPS), a novel theoretical framework that challenges the conventional understanding of wave function collapse in quantum mechanics. The core concept posits that the probability distribution of a particle's existence itself possesses a structured spectrum, denoted as *Sp(ω)*. This spectrum represents a dynamic and inherently probabilistic organization of potential locations rather than a sudden, deterministic collapse of a pre-existing wave function. The theoretical underpinnings are developed through a rigorous mathematical treatment of quantum mechanics, emphasizing the continuous nature of probability. The experimental validation of PEPS relies on high-precision interference experiments, designed to reveal the underlying structure of the probability distribution. The implications of PEPS extend to a re-evaluation of wave function interpretation and could potentially unlock new insights into the fundamental nature of reality. The central equation for the PEPS is: *Sp(ω) = ∫ |ψ(x, ω)|^2 dx* Where: * *Sp(ω)* is the Particle Existence Probability Spectrum. * *|ψ(x, ω)|^2* is the probability density of finding the particle at position *x* with frequency *ω*. * *∫* denotes the integral over all possible positions *x*.
This paper introduces the Quantum Field Boundary Generator (QFBG), a theoretical framework proposing that particle boundaries are not fixed physical constructs, but rather emergent properties arising from the self-organizing dynamics of quantum fields. The core concept involves utilizing a field operator, denoted as [ψ], to dynamically generate and maintain particle boundaries. This approach offers a potential explanation for the observed deterministic nature of particle behavior within the framework of quantum mechanics and possesses potential implications for high-energy physics and could be considered for Nobel Prize recognition. The QFBG avoids the need for pre-defined particle shapes or external constraints, allowing for a purely field-based description of particle existence and interaction. The mathematics presented here focuses on the fundamental equations governing the field and the boundary generation process, offering a novel perspective on particle formation and stability. This framework presents a significant departure from traditional models of particle physics, suggesting a more intrinsic and dynamically-determined nature of matter.
Quantum entanglement, a bizarre phenomenon where two or more particles become linked and share the same fate, has captivated physicists and philosophers alike. This paper explores the potential of leveraging quantum entanglement to revolutionize probability theory and, consequently, quantum computation. We propose a novel probabilistic model predicated on the inherent nature of quantum entanglement, aiming to provide a deeper understanding of probability and enhance quantum machine learning capabilities. The core of this work lies in establishing a probabilistic model intrinsically linked to the quantum entanglement mechanism, offering a fundamentally new approach to interpreting and optimizing quantum computations. The model will be designed to function as a computational foundation for quantum algorithms, addressing the limitations of traditional probabilistic methods. This paper will detail the theoretical framework, preliminary implementation, and potential applications of this new model.
This paper introduces the Quantum Measurement Structure Field (QMSF) hypothesis, a novel framework aimed at resolving longstanding challenges in quantum measurement theory. The core concept posits that during a quantum measurement, a structured field, denoted as [Sm(x,t)], emerges, which fundamentally determines the outcome's stability and the observed result. This framework moves beyond the traditional interpretations of wave function collapse and probabilistic outcomes, proposing a dynamic and structured reality shaped by the measurement process itself. The paper outlines the foundational principles of QMSF, highlighting its potential to address fundamental questions regarding the nature of reality, the role of the observer, and the mechanisms underlying quantum measurement. Further development and investigation of QMSF are presented as a potential pathway towards a deeper understanding of quantum mechanics and a significant advance in the field. ---
This paper explores the concept of a probabilistic wave function – a fundamental representation of the observable universe – designed to model long-term evolution of complex systems through probabilistic interplay. We propose a novel approach to wave function construction, focusing on a dynamically evolving system that captures the probabilities of numerous variables. The core claim is that this approach, while requiring significant computational resources, could provide a deeper understanding of fundamental physics and allow for modeling of complex systems with unprecedented predictive capabilities. This work addresses the limitations of traditional probability models by integrating a self-modifying wave function that evolves over time, representing the probabilities of different outcomes.
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.