Machine learning, particularly deep neural networks, has been widely used in high-energy physics, demonstrating remarkable results in various applications. Furthermore, the extension of machine learning to quantum computers has given rise to the emerging field of quantum machine learning. In this paper, we propose the Quantum Complete Graph Neural Network (QCGNN), which is a variational...
Quantum Machine Learning (QML) faces significant challenges, particularly in encoding classical data and the reliance on quantum hardware for inference, limiting its practical applications. Meanwhile, classical large language models (LLMs) demand immense computational resources and exhibit low training efficiency, leading to substantial cost and scalability concerns. This talk will introduce...
