Quantum-Inspired Neural Networks for Advanced AI Applications - A Scholarly Review of Quantum Computing Techniques in Neural Network Design
Keywords:
Quantum computing, Quantum-inspired neural networks, Neural network design, Advanced AI applications, Quantum computing techniquesAbstract
Quantum computing has emerged as a promising paradigm for enhancing artificial intelligence (AI) capabilities, particularly in the realm of neural networks. Quantum-inspired neural networks (QINNs) leverage principles from quantum computing to improve the efficiency and performance of traditional neural networks. This paper provides a comprehensive review of QINNs for advanced AI applications, focusing on the integration of quantum computing techniques in neural network design. We discuss the key concepts behind quantum computing, the principles of QINNs, and their potential advantages over classical neural networks. Furthermore, we examine the current state of research in QINNs, highlighting notable advancements and challenges. Through this review, we aim to provide insights into the future prospects of QINNs and their role in shaping the next generation of AI technologies.
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