pLazyQML: a parallel package for efficient execution of QML models on classical computers

Abstract Quantum machine learning, positioned at the convergence of quantum computing and artificial intelligence, is an emerging and highly promising field, primarily due to its potential to enhance the performance of classical machine learning systems. As this area is developing at an exceptionally rapid pace, it is essential to remain up to date with the latest advancements and research. This paper introduces pLazyQML , a software package designed to accelerate, automate, and streamline experimentation with quantum machine learning models on classical computers. pLazyQML reduces the complexity and time required for developing and testing quantum-enhanced machine learning models. Comprehensive experiments on established models and datasets demonstrate the efficiency, scalability, and workflow simplification provided by pLazyQML , making it a valuable tool for researchers and practitioners in quantum machine learning.