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:最近,一些研究提出了量子联邦学习(QFL)框架。例如,谷歌的TensorFlow Quantum (TFQ)和TensorFlow Federated (TFF)库已被部署实现QFL。然而,开发人员大多尚未熟悉量子计算(QC)库和框架。提供一个对基础QC和联邦学习(FL)库提供抽象层次的面向领域的建模语言(DSML)将是有益的。这可以使实践者在部署量子机器学习(QML)的同时高效地执行软件开发和数据科学任务。在这篇立场论文中,我们建议扩展现有的面向机器学习(ML)启用系统的面向领域的建模驱动工具,如MontiAnna、ML-Quadrat和GreyCat,以支持QFL。