Deep Learning with Quantum and Classical Parameters

Explore a comprehensive presentation on 'End-to-end Differentiation' with QML/QiML Artificial Intelligence, focusing on three key aspects. Discover how quantum-inspired workflows using CPUs, GPUs, or TPUs can optimize parameters of quantum and classical deep learning networks. Examine the 2019 'Quantum transfer learning' model by Andrea Mari, comparing untrained and fully trained ResNet models with and without quantum circuits. Investigate the steps to troubleshoot mainstream 'End-to-end Differentiation' models using 'Simulator specific algorithms' with quantum and classical parameters for improved AI accuracies and new utilities. Gain insights into the proposed term 'QML/QiML' and its significance in the industry. Learn from innovations by PennyLane, Qiskit, and PyTorch to enhance QiML workflows for important applications.

Deep Learning with Quantum and Classical Parameters