Recently, several studies have proposed frameworks for Quantum Federated Learning (QFL). For instance, the Google TensorFlow Quantum (TFQ) and TensorFlow Federated (TFF) libraries have been deployed for realizing QFL. However, developers, in the main, are not as yet familiar with Quantum Computing (QC) libraries and frameworks. A Domain-Specific Modeling Language (DSML) that provides an abstraction layer over the underlying QC and Federated Learning (FL) libraries would be beneficial. This could enable practitioners to carry out software development and data science tasks efficiently while deploying the state of the art in Quantum Machine Learning (QML). In this position paper, we propose extending existing domain-specific Model-Driven Engineering (MDE) tools for Machine Learning (ML) enabled systems, such as MontiAnna, ML-Quadrat, and GreyCat, to support QFL.

翻译：近期，多项研究提出了量子联邦学习（QFL）框架。例如，谷歌TensorFlow Quantum（TFQ）与TensorFlow Federated（TFF）库已被部署用于实现QFL。然而，目前大多数开发者对量子计算（QC）库和框架尚不熟悉。一种能够为底层量子计算与联邦学习（FL）库提供抽象层的领域特定建模语言（DSML）将具有重要价值。这可使实践者在部署量子机器学习（QML）前沿技术的同时，高效完成软件开发与数据科学任务。在本立场论文中，我们提出扩展现有面向机器学习（ML）系统的领域特定模型驱动工程（MDE）工具（如MontiAnna、ML-Quadrat及GreyCat），使其支持QFL。