Quantum Machine Learning with Python : Using Cirq from Google Research and IBM Qiskit / by Santanu Pattanayak.

Format:

Book

Author/Creator:

Pattanayak, Santanu, author.

Language:

English

Subjects (All):

Artificial intelligence.

Programming languages (Electronic computers).

Open source software.

Artificial Intelligence.

Programming Language.

Open Source.

Local Subjects:

Artificial Intelligence.

Programming Language.

Open Source.

Physical Description:

1 online resource (XIX, 361 p. 79 illus.)

Edition:

1st ed. 2021.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2021.

Summary:

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. You will: 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.

Contents:

Chapter 1: Introduction to Quantum Mechanics and Quantum Computing

Chapter 2: Mathematical Foundations and Postulates of Quantum Computing

Chapter 3: Introduction to Quantum Algorithms

Chapter 4: Quantum Fourier Transform Related Algorithms

PART 2 Chapter 5: Introduction to Quantum Machine Learning

Chapter 6: Quantum Deep Learning and Quantum Optimization Based Algorithms

Chapter 7: Quantum Adiabatic Processes and Quantum based Optimization. .

Notes:

Includes index.

ISBN:

9781484265222

148426522X

OCLC:

1244120897