num_pages: 0
description: The eld of quantum computing and deep machine learning algorithms are undergoing a rapid expansion. There are number of breakthrough applications in recent years. The main aim of the book is to bridge classical and quantum machine learning algorithms within a unied framework of the latest development on quantum computers. The book examined limitations and advantages of different machine learning algorithms in classical and quantum computing frameworks. It explained the ways to leverage the quantum properties of superposition entanglement and tunneling in the context of machine learning and artificial intelligence applications. The book explained in depth the concepts of Quantum Algorithms Quantum Programming Quantum Neural Networks Quantum Parametric Circuits Quantum back-propagation principles Quantum Support Vector Machines Quantum CNN Quantum Restricted Boltzmann Machines Quantum LSTM Quantum RNN Quantum Deep Learning and Quantum Reinforcement Learning. It also explains the details of Quantum Principal Component Analysis Quantum State Learning Quantum Meta-Learning (QML) Quantum Dynamical Descent (QDD) Quantum Approximate Optimization Algorithm (QAOA) Quantum Adiabatic Algorithm (QAA). Finally the book discuses the Quantum Learning in different hybrid architectures.
language: None
title: Quantum Computing Algorithms for Artificial Intelligence
publisher: None