Quantum Machine Learning with Python

Using Cirq from Google Research and IBM Qiskit

Santanu Pattanayak

• Publisher: Apress
• Year: 2021
• Print ISBN: 9781484265215
• eBook ISBN: 9781484265222
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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 foundationbase is set, you'll delve deep into Quantum based algorithms including Quantum Fouriertransform, 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

Table of contents

• Cover
• Front Matter
• 1. Introduction to Quantum Computing
• 2. Mathematical Foundations and Postulates of Quantum Computing
• 3. Introduction to Quantum Algorithms
• 4. Quantum Fourier Transform and Related Algorithms
• 5. Quantum Machine Learning
• 6. Quantum Deep Learning
• 7. Quantum Variational Optimization and Adiabatic Methods
• Back Matter