De novo peptide design exhibits great potential in materials engineering, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no known peptide binders for many plastics—a gap that can be filled with de novo design. Current computational methods for peptide design exhibit limitations in sampling and scaling that could be addressed with quantum computing. Hybrid quantum-classical methods can leverage complementary strengths of near-term quantum algorithms and classical techniques for complex tasks like peptide design. This work introduces a hybrid quantum-classical generative framework for designing plastic-binding peptides combining variational quantum circuits with a variational autoencoder network. We demonstrate the framework’s effectiveness in generating peptide candidates, evaluate its efficiency for property-oriented design, and validate the candidates with molecular dynamics simulations. This quantum computing–based approach could accelerate the development of biomolecular tools for environmental and biomedical applications while advancing the study of biomolecular systems through quantum technologies.

中文翻译：

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使用基于变分量子电路的混合量子经典方法设计微塑料结合肽


从头肽设计在材料工程中表现出巨大的潜力，特别是对于使用塑料结合肽来帮助修复微塑料污染。许多塑料没有已知的肽结合剂——这一空白可以用从头设计来填补。当前肽设计的计算方法在采样和缩放方面存在局限性，这可以通过量子计算来解决。混合量子经典方法可以利用近期量子算法和经典技术的互补优势来完成肽设计等复杂任务。这项工作介绍了一个混合量子-经典生成框架，用于设计结合多肽，将变分量子电路与变分自动编码器网络相结合。我们证明了该框架在生成候选肽方面的有效性，评估其面向特性的设计效率，并通过分子动力学模拟验证候选肽。这种基于量子计算的方法可以加速用于环境和生物医学应用的生物分子工具的开发，同时通过量子技术推进生物分子系统的研究。