The increasing global burden of chronic diseases, which accounts for nearly 70% of
healthcare costs, has driven a paradigm shift toward preventive healthcare strategies …

Quantum computing offers a promising frontier for advancing machine learning by
leveraging quantum principles to enhance feature engineering, a critical step in the data …

Quantum error correction (QEC) is a critical component in the development of robust
quantum computing systems, crucial for advancing the capabilities of artificial intelligence …

ABSTRACT Quantum Neural Networks (QNNs) represent a convergence of quantum
computing and artificial neural networks, offering novel computational paradigms that …

ABSTRACT Quantum Generative Adversarial Networks (QGANs) represent a significant
advancement in the intersection of quantum computing and machine learning. By leveraging …

ABSTRACT Natural Language Processing (NLP) has seen remarkable advancements with
classical machine learning models, but scaling computational efficiency remains a …

ABSTRACT Quantum Reinforcement Learning (QRL) merges the principles of quantum
computing with reinforcement learning (RL) to enhance the efficiency and effectiveness of …

Quantum machine learning (QML) is an emerging field that integrates quantum computing
with machine learning, promising to revolutionize traditional computational tasks by …

Abstract Graph Neural Networks (GNNs) have emerged as powerful tools for learning
representations of graph-structured data, but their computational complexity and scalability …

Recent advancements in quantum computing have opened new avenues for optimizing
neural network training processes, promising significant improvements over classical …