Evidence map›Paper›PMID 42885048›Full record

ReviewPolymers2026

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

Min Ook Kim

Abstract readReview
In one paragraph

Review in Polymers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Min Ook KimDepartment of Civil Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea.ORCID 0000-0002-9506-1505

Funding

Korea SMEs and Startups Agency G0900002616
6 · The paper itself

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.

Indexed as

density functional theoryhybrid quantum–classical computingmaterials designmaterials informaticsmolecular dynamicsmultiscale modellingpolymeric compositesquantum algorithmsquantum computingquantum machine learning

Identifiers

PMID42885048
PMCPMC13666856

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.