Quantum machine learning with Python : using Cirq from Google Research and IBM Qiskit / Santanu Pattanayak.
2021
QA76.889
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Details
Title
Quantum machine learning with Python : using Cirq from Google Research and IBM Qiskit / Santanu Pattanayak.
Author
Pattanayak, Santanu, author.
ISBN
9781484265222 (electronic bk.)
148426522X (electronic bk.)
9781484265215
1484265211
148426522X (electronic bk.)
9781484265215
1484265211
Published
[Berkeley, CA] : Apress, [2021]
Language
English
Description
1 online resource (xix, 361 pages) : illustrations
Other Standard Identifiers
10.1007/978-1-4842-6522-2 doi
Call Number
QA76.889
Dewey Decimal Classification
006.3/843
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.
Note
Includes index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed April 13, 2021).
Available in Other Form
Print version: 9781484265215
Print version: 9781484265239
Print version: 9781484265239
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Table of 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.
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.