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Based on Quantum Many-Body Entanglement: Materials Design

Jincheng Zhang

Published Aug 28, 2026• 0 citations
Publisher

Abstract

This research explores a novel approach to materials design predicated on harnessing the power of quantum many-body entanglement. Traditional materials design methodologies often rely on classical mechanics and empirical approaches, frequently yielding limited predictive capabilities, particularly for complex materials exhibiting quantum behavior. This work proposes a framework where the intricate interactions between quantum particles, specifically entanglement, are deliberately incorporated into the design process. We posit that by understanding and manipulating these entanglement correlations, we can engineer materials with tailored properties not achievable through conventional methods. This paper outlines the theoretical foundations, computational strategies, and potential applications of this approach, highlighting its capacity to revolutionize materials discovery and development. We introduce a methodology centered on simulating and predicting material behavior based on quantum materials theory and advanced material design techniques, guided by the fundamental principles of quantum entanglement. The ultimate goal is to translate this theoretical understanding into tangible materials with precisely controlled characteristics.

DOI
10.5281/zenodo.22139001

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Quantum-enhanced lattice-based simulations represent a significant advancement in materials design and computational modeling, offering the potential to dramatically accelerate the discovery of novel materials with tailored properties. This paper explores a novel approach to lattice simulations leveraging quantum computing to enhance accuracy and computational efficiency. We introduce a new quantum algorithm designed to address the limitations of classical methods, specifically focusing on the intricate interplay of many-body interactions within complex lattice structures. The core mechanism centers around utilizing quantum algorithms to directly explore the potential energy landscape of the system, leading to a more precise and efficient representation of material behavior. This paper presents a detailed overview of the algorithm, its theoretical underpinnings, and preliminary experimental results demonstrating its potential to revolutionize materials science.
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