Paper Title
QUANTUM NEURAL NETWORKS FOR ASTROPHYSICAL SIGNAL CLASSIFICATION: A COMPREHENSIVE REVIEW

Abstract
This review focuses on quantum computing as a revolutionary paradigm that has the potential to solve computational problems which are otherwise intractable using classical computing systems. One of the most interesting developments in this area is Quantum Machine Learning (QML), which combines quantum computing with machine learning. This articlepresents a comprehensive review of quantum neural network (QNN) approaches, with a particular focus on the classification of astrophysical signals and the identification of cosmic events. Starting from the underlying theories and recent studies on applications, it presents a synthesis of research on Quantum Convolutional Neural Networks (QCNNs), quantum-classical hybrids, and machine learning-enabled quantum sensing systems. This review examines the approaches, data sets, circuit layouts, benchmarking methods, and performance metrics presented in the literature. A summary table is also provided, which presents the key contributions of different studies. In addition, this review also covers the challenges of noise, scalability, barren plateaus, and data encoding, and presents future directions of research. This review hopes to provide a useful resource for researchers and students working in the area of quantum computing, machine learning, and astrophysics. Keywords - Quantum Neural Networks, Quantum Machine Learning, Quantum Convolutional Neural Networks, Astrophysical Signal Processing, Cosmic Event Detection, Hybrid Quantum-Classical Models, Gravitational Wave Observatories