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Quantum-Enhanced Constraint Optimization for Polymorphic Material Design

Jincheng Zhang

Published Sep 4, 2026• 0 citations
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Abstract

This paper presents a novel approach to materials design utilizing Quantum-Enhanced Constraint Optimization (QCO). Polymorphic materials, characterized by their varying crystalline structures and properties, present a significant challenge in materials science due to the complexity of tailoring their characteristics. Traditional optimization methods often struggle with the inherent non-linearity and high dimensionality of these systems. This work introduces a QCO algorithm, incorporating quantum mechanics principles to efficiently identify optimal material configurations, leading to enhanced material properties. The core mechanism centers on using quantum-guided optimization to navigate complex parameter spaces, accelerating the discovery of materials with precisely controlled structures. We demonstrate the effectiveness of this method through a series of simulations and preliminary experiments, showcasing significant improvements in material properties compared to conventional optimization techniques.

DOI
10.5281/zenodo.22289334

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Quantum-enhanced material design offers the potential to accelerate the discovery of novel materials with unprecedented properties, surpassing traditional approaches. This paper presents a novel optimization algorithm, Quantum-Enhanced Material Design via Lattice-Based Optimization (QLBO), which leverages quantum mechanics to efficiently explore the chemical space of material configurations. We demonstrate the efficacy of QLBO in rapidly identifying promising material candidates through a variational quantum eigensolver (VQE) guided search. This approach provides a direct path to optimization based on quantum mechanics, addressing limitations of conventional methods. The algorithm's core mechanism utilizes VQE to efficiently navigate complex chemical spaces, resulting in accelerated design cycles and the discovery of materials with precisely tailored properties.
Quantum-driven lattice parameter optimization is a novel approach to materials science that leverages the power of quantum computing to efficiently explore the parameter space of crystal structures. Traditional methods, while effective, often face computational bottlenecks, limiting the scope of potential discoveries. This paper proposes a quantum-enhanced optimization strategy utilizing quantum annealing and variational quantum eigensolver (VQE) techniques to systematically search for optimal lattice parameters, potentially leading to the identification of novel material properties. The core mechanism centers around quantum algorithms designed to navigate the complex parameter space, offering a potential paradigm shift in materials discovery. This work addresses the challenges of computational complexity and provides a framework for harnessing quantum mechanics to accelerate materials design. This research explores the feasibility of using quantum algorithms to optimize lattice parameters, focusing on the potential to uncover new material characteristics and advancements.
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