Quantum Gate Neural Networks – A Walkthrough from Quantum-Inspired To Quantum Domain

This quantum computing seminar explores the implementation of neural networks within quantum computing frameworks. It begins with a refresher on classical neural networks, followed by an introduction to quantum-inspired soft computing, including the development of a quantum neuron model and the Quantum-Inspired Backpropagation Neural Network (QBPNN) architecture. Key topics include the operational principles of QBPNN, its quantum backpropagation algorithm, and applications for image denoising and deblurring in comparison to classical Multi-Layer Perceptron (MLP) architectures. The talk further delves into the TensorFlow Quantum Framework (TQF), Parameterised Quantum Circuits (PQCs), and Quantum Convolutional Neural Networks (QCNNs). It concludes by highlighting the advantages of Quantum Neural Networks (QNNs) over classical approaches, offering insights into the future of quantum computing in machine learning.

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