Quantum Driven Machine Learning

Jincheng Zhang

Quantum Information-Driven Machine Learning Algorithm

Ivan B. Djordjević

Armin Moin, Atta Badii, Moharram Challenger

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.

Jincheng Zhang

Quantum Topology-Driven Machine Learning explores the potential of combining quantum topology theory with machine learning to enhance model generalization. This paper investigates how utilizing quantum topology as a learning objective can improve model robustness and performance. We outline the core mechanism, detailing how quantum entanglement and topological phases are leveraged to create novel learning paradigms. The research aims to demonstrate a shift from traditional data-dependent models towards a more adaptable and potentially more powerful approach, specifically focusing on enhancing the generalization capabilities of machine learning algorithms. The paper concludes by discussing future research directions and potential applications within the field of artificial intelligence.

Jacob Biamonte, Péter Wittek, Nicola Pancotti +3 more