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Quantum Computing for Polymeric Composites: Principles, Methods, Applications, and Future Perspectives.

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Title: Quantum Computing for Polymeric Composites: Principles, Methods, Applications, and Future Perspectives.
Authors: Kim, Min Ook1 (AUTHOR)
Source: Polymers (20734360). Oct2026, Vol. 18 Issue 19, p2418. 29p.
Subjects: Quantum computing, Polymeric composites, Multiscale modeling, Fiber-matrix interfaces, Quantum computers, Polymer structure
Abstract: Polymeric composites have become indispensable engineering materials owing to their exceptional mechanical performance, lightweight characteristics, corrosion resistance, and design flexibility. Conventional computational approaches, including density functional theory (DFT), molecular dynamics (MD), finite element analysis (FEA), and machine learning (ML), have substantially improved the understanding of polymer structure–property relationships. However, the increasing complexity of multiscale polymeric systems, intricate fiber–matrix interactions, and the computational cost associated with accurately modelling quantum-mechanical phenomena continue to present significant challenges for classical computing. This review provides a comprehensive assessment of the emerging role of quantum computing in polymeric composite research. It first introduces the fundamental principles of quantum computing, including qubits, quantum gates, quantum circuits, representative quantum algorithms, and contemporary hardware platforms. Subsequently, the review examines conventional computational methods used for modelling polymeric composites and discusses how quantum computing may complement and accelerate these established approaches. Recent and prospective applications in polymer electronic structure calculations, fiber–matrix interface engineering, nanocomposite design, multiscale modelling, and intelligent materials discovery are critically evaluated. Particular emphasis is placed on hybrid quantum–classical frameworks that integrate quantum computing with artificial intelligence, materials informatics, and high-throughput computational workflows as candidate tools for polymer design. The evidence is separated into polymer-specific hardware demonstrations, simulator-based studies, transferable non-polymer benchmarks, and prospective applications. Existing studies establish feasibility for selected tasks; they do not establish general end-to-end quantum advantage for realistic polymeric composites. [ABSTRACT FROM AUTHOR]
Copyright of Polymers (20734360) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Quantum+computing%22">Quantum computing</searchLink><br /><searchLink fieldCode="DE" term="%22Polymeric+composites%22">Polymeric composites</searchLink><br /><searchLink fieldCode="DE" term="%22Multiscale+modeling%22">Multiscale modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Fiber-matrix+interfaces%22">Fiber-matrix interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+computers%22">Quantum computers</searchLink><br /><searchLink fieldCode="DE" term="%22Polymer+structure%22">Polymer structure</searchLink>
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  Data: Polymeric composites have become indispensable engineering materials owing to their exceptional mechanical performance, lightweight characteristics, corrosion resistance, and design flexibility. Conventional computational approaches, including density functional theory (DFT), molecular dynamics (MD), finite element analysis (FEA), and machine learning (ML), have substantially improved the understanding of polymer structure–property relationships. However, the increasing complexity of multiscale polymeric systems, intricate fiber–matrix interactions, and the computational cost associated with accurately modelling quantum-mechanical phenomena continue to present significant challenges for classical computing. This review provides a comprehensive assessment of the emerging role of quantum computing in polymeric composite research. It first introduces the fundamental principles of quantum computing, including qubits, quantum gates, quantum circuits, representative quantum algorithms, and contemporary hardware platforms. Subsequently, the review examines conventional computational methods used for modelling polymeric composites and discusses how quantum computing may complement and accelerate these established approaches. Recent and prospective applications in polymer electronic structure calculations, fiber–matrix interface engineering, nanocomposite design, multiscale modelling, and intelligent materials discovery are critically evaluated. Particular emphasis is placed on hybrid quantum–classical frameworks that integrate quantum computing with artificial intelligence, materials informatics, and high-throughput computational workflows as candidate tools for polymer design. The evidence is separated into polymer-specific hardware demonstrations, simulator-based studies, transferable non-polymer benchmarks, and prospective applications. Existing studies establish feasibility for selected tasks; they do not establish general end-to-end quantum advantage for realistic polymeric composites. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Polymers (20734360) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/polym18192418
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        Text: English
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      – SubjectFull: Multiscale modeling
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              Text: Oct2026
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