Quickly scale up to Quantum computing and Quantum machine learning foundations and related
mathematics and expose them to different use cases that can be solved through Quantum based
algorithms.This book explains Quantum Computing which leverages the Quantum mechanical
properties sub-atomic particles. It also examines Quantum machine learning which can help
solve some of the most challenging problems in forecasting financial modeling genomics
cybersecurity supply chain logistics cryptography among others. You'll start by reviewing the
fundamental concepts of Quantum Computing such as Dirac Notations Qubits and Bell state
followed by postulates and mathematical foundations of Quantum Computing. Once the foundation
base is set you'll delve deep into Quantum based algorithms including Quantum Fourier
transform phase estimation and HHL (Harrow-Hassidim-Lloyd) among others. You'll then be
introduced to Quantum machine learning and Quantum deep learning-based algorithms along with
advanced topics of Quantum adiabatic processes and Quantum based optimization. Throughout the
book there are Python implementations of different Quantum machine learning and Quantum
computing algorithms using the Qiskit toolkit from IBM and Cirq from Google Research. What
You'll Learn Understand Quantum computing and Quantum machine learning Explore varied domains
and the scenarios where Quantum machine learning solutions can be applied Develop expertise in
algorithm development in varied Quantum computing frameworks Review the major challenges of
building large scale Quantum computers and applying its various techniques Who This Book Is For
Machine Learning enthusiasts and engineers who want to quickly scale up to Quantum Machine
Learning
