Hybrid Quantum-Classical Neural Networks: A New Frontier in AI and Quantum Computing
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P103Keywords:
Hybrid Quantum-Classical Neural Networks, Quantum Machine Learning, Quantum Variational Circuit, Quantum Feature Extraction, Hybrid Optimization, Quantum-Classical Interfaces, Quantum Approximate Optimization Algorithm, Scalable Quantum Hardware, Quantum Error Mitigation, Ethical AI in Quantum ComputingAbstract
Hybrid Quantum-Classical Neural Networks (HQCNNs) represent a novel and promising approach that integrates the strengths of classical and quantum computing to advance artificial intelligence (AI). This paper provides a comprehensive overview of HQCNNs, including their theoretical foundations, practical implementations, and potential applications. We delve into the fundamental concepts of quantum computing, classical neural networks, and the hybrid model. The paper also explores the challenges and opportunities associated with HQCNNs, and discusses recent advancements in the field. Finally, we present a detailed case study and a novel algorithm to illustrate the practical implementation of HQCNNs. The findings suggest that HQCNNs have the potential to revolutionize various domains, from drug discovery to financial modeling, by leveraging the unique capabilities of quantum computing
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