Quantum-Inspired Lattice Field Theory for Complex Systems

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum-Inspired Lattice Field Theory (QILFT) presents a novel theoretical framework for modeling complex systems by integrating quantum field theory principles with lattice geometry. This approach aims to capture the emergent behavior of these systems through the interplay of entanglement and geometric constraints. The core claim is to develop a robust and adaptable model capable of predicting the evolution of complex systems across diverse physical regimes. This work explores the potential of utilizing entanglement as a fundamental building block for a probabilistic field theory, emphasizing its role in generating complex dynamics. We detail the theoretical basis, mathematical formulation, and potential applications of QILFT, highlighting its unique approach to addressing challenges inherent in complex systems modeling.

DOI: 10.5281/zenodo.22155469

Related papers

Title: Quantum-Inspired Constraint Field (QICF)

Jincheng Zhang

Quantum-Inspired Constraint Field (QICF) is a novel theoretical framework designed to address the challenges of understanding and predicting the evolution of dynamical systems, particularly those exhibiting complex and stable configurations. This work proposes a method based on the application of quantum field theory concepts, specifically entanglement and tunneling, to define constraints within dynamical systems. The core mechanism involves utilizing these concepts to create a "quantum-inspired" set of constraints, allowing for the prediction of stable and complex configurations – a significant departure from traditional approaches. This framework holds the potential to unlock new insights into control theory and provide a powerful tool for designing robust and predictable systems. The proposed method offers a fundamentally different perspective on constraint definition, potentially leading to significant advances in the modeling and control of complex phenomena.

Sep 6, 20260 citations

Emergent Quantum Field Theory in Complex Systems

Jincheng Zhang

This paper explores the theoretical framework for predicting and explaining the emergence of quantum field theory within complex systems. We propose a novel mathematical formalism, the "Quantum Resonance Matrix" (QRM), designed to capture the intricate interplay between quantum mechanics and complex system dynamics. The core claim is that this framework allows for the prediction of quantum behavior within these systems, offering a path toward a deeper understanding of fundamental phenomena. We detail the mathematical foundations of the QRM, outlining its key components and the potential for applying it to various complex systems. The paper concludes with a discussion of the challenges and future directions for research in this area.

Sep 3, 20260 citations

Quantum-Inspired Geometric Modeling for Complex Systems

Jincheng Zhang

Quantum-inspired geometric modeling aims to enhance the accuracy of simulations and predictions across a diverse range of complex systems by leveraging the principles of quantum mechanics and geometric representation. This paper presents a novel framework for modeling complex systems using quantum-inspired geometric concepts, focusing on the creation of a 'quantum state' representation that facilitates more precise and reliable predictions. The core mechanism involves incorporating quantum-inspired geometric techniques into the system's geometry, resulting in a shift towards more accurate and robust simulations. This research addresses limitations inherent in traditional modeling approaches and offers a promising avenue for advancing predictive capabilities in various fields. The proposed framework provides a foundation for improved understanding and control within complex systems.

Sep 3, 20260 citations

Title: Universal Simulation of Complex Fluid Dynamics via Quantum Field Theory

Jincheng Zhang

This paper explores the potential of quantum field theory (QFT) to revolutionize the simulation of complex fluid dynamics, offering a pathway to significantly enhanced computational power and the capture of emergent behaviors previously unattainable through conventional methods. We introduce a novel framework predicated on quantum entanglement to model fluid particle dynamics, aiming to overcome the computational bottlenecks inherent in traditional fluid simulations. The core claim is to develop a QFT-based simulation system capable of handling increasingly complex fluid phenomena with unprecedented fidelity. The paper details the proposed methodology, outlining the mathematical foundations and preliminary experimental results demonstrating the framework's potential. We emphasize the role of quantum entanglement as a key enabling principle for achieving this transformative approach.

Sep 1, 20260 citations

Title: Quantum Entanglement-Based Probability Space Modeling (QE-PSM)

Jincheng Zhang

Quantum entanglement, a cornerstone of quantum mechanics, offers a unique perspective on probabilistic outcomes and the behavior of complex systems. This paper explores the application of quantum entanglement to probability space modeling, aiming to extend classical probability models to encompass the inherent uncertainty and emergent behaviors observed in systems exhibiting quantum phenomena. We propose a novel algorithm, Quantum-Inspired Probability Space Modeling (QE-PSM), that leverages entanglement as a fundamental probability field, allowing for a more accurate and dynamic representation of random processes. The core mechanism focuses on incorporating entanglement into a probabilistic framework, potentially surpassing the limitations of conventional methods in simulating complex systems. This research addresses the inherent challenges of modeling uncertainty and provides a promising avenue for advancing simulations in diverse fields, including physics, chemistry, and materials science.

Sep 3, 20260 citations

拓扑量子纠缠的自适应几何建模

Jincheng Zhang

This paper explores the potential of utilizing quantum entanglement's self-adaptive geometry to construct a dynamic and self-adaptive mathematical model for describing complex systems' topology, and predicting their behavior. Specifically, we investigate how quantum entanglement's inherent properties can be leveraged to dynamically adjust geometric structures, enabling precise modeling of intricate system dynamics. The core mechanism involves integrating quantum entanglement with geometric transformations, creating a framework for capturing the evolution of complex system behavior. We demonstrate the feasibility of this approach through a conceptual and preliminary analysis, highlighting the significant gap in current research towards this domain. This work represents a novel investigation into applying quantum mechanics to the modeling of topological phenomena, specifically focusing on the intricate interplay between geometry and entanglement.

Aug 29, 20260 citations