國立高雄大學圖資館 |

Language: English

Back

Quantum machine learning with Python...

Pattanayak, Santanu.

Quantum machine learning with Pythonusing Cirq from Google Research and IBM Qiskit /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantum machine learning with Pythonby Santanu Pattanayak.
Reminder of title:	using Cirq from Google Research and IBM Qiskit /
Author:	Pattanayak, Santanu.
Published:	Berkeley, CA :Apress :2021.
Description:	xix, 361 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Quantum computing.
Online resource:	https://doi.org/10.1007/978-1-4842-6522-2
ISBN:	9781484265222$q(electronic bk.)

Quantum machine learning with Pythonusing Cirq from Google Research and IBM Qiskit /
Pattanayak, Santanu.

Quantum machine learning with Pythonusing Cirq from Google Research and IBM Qiskit /[electronic resource] :by Santanu Pattanayak. - Berkeley, CA :Apress :2021. - xix, 361 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9781484265222$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-6522-2doiSubjects--Topical Terms:

725269
Quantum computing.

LC Class. No.: QA76.889

Dewey Class. No.: 006.3843

Quantum machine learning with Pythonusing Cirq from Google Research and IBM Qiskit /
LDR:03118nmm a2200325 a 4500 001 599880
003 DE-He213
005 20210719135710.0
006 m d
007 cr nn 008maaau
008 211027s2021 cau s 0 eng d
020 $a 9781484265222$q(electronic bk.)
020 $a 9781484265215$q(paper)
024 7 $a 10.1007/978-1-4842-6522-2 $2 doi
035 $a 978-1-4842-6522-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.889
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3843 $2 23
090 $a QA76.889 $b .P315 2021
100 1 $a Pattanayak, Santanu. $3 798242
245 1 0 $a Quantum machine learning with Python $h [electronic resource] : $b using Cirq from Google Research and IBM Qiskit / $c by Santanu Pattanayak.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xix, 361 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Quantum computing. $3 725269
650 0 $a Machine learning. $3 188639
650 0 $a Python (Computer program language) $3 215247
650 0 $a Artificial intelligence. $3 194058
650 0 $a Computer software. $3 180007
650 0 $a Open source software. $3 200208
650 0 $a Computer programming. $3 181992
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Professional Computing. $3 763344
650 2 4 $a Open Source. $3 758930
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6522-2
950 $a Professional and Applied Computing (SpringerNature-12059)