Quantum Neural Networks with Novel Architectures

EasyChair Preprint 14864

Abstract

Quantum Neural Networks (QNNs) represent a convergence of quantum computing and artificial neural networks, offering novel computational paradigms that surpass classical limits. This study explores the development of QNNs with innovative architectures designed to enhance learning efficiency, scalability, and computational speed. By leveraging quantum superposition, entanglement, and interference, these novel QNN architectures enable faster optimization and improved model performance on complex, high-dimensional data. Our research introduces new quantum gate configurations and hybrid quantum-classical frameworks to mitigate the challenges posed by quantum noise and decoherence. We demonstrate the application of these architectures in solving problems such as classification, pattern recognition, and optimization in quantum machine learning. Comparative results with classical deep learning models reveal the potential of QNNs to revolutionize fields requiring massive computational power, like drug discovery, cryptography, and financial modeling. The findings underscore the transformative role of quantum computing in advancing neural network capabilities, paving the way for future innovations in quantum artificial intelligence.

Keyphrases: Computational paradigms, Quantum Neural Networks, novel architectures

@booklet{EasyChair:14864,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {Quantum Neural Networks with Novel Architectures},
  howpublished = {EasyChair Preprint 14864},
  year      = {EasyChair, 2024}}