國立虎尾科技大學 |

Quantum Machine Learning with Python = Using Cirq from Google Research and IBM Qiskit /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Quantum Machine Learning with Python/ by Santanu Pattanayak.
其他題名:	Using Cirq from Google Research and IBM Qiskit /
作者:	Pattanayak, Santanu.
面頁冊數:	XIX, 361 p. 79 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Open Source. -
電子資源:	https://doi.org/10.1007/978-1-4842-6522-2
ISBN:	9781484265222

Quantum Machine Learning with Python = Using 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. - 1st ed. 2021. - XIX, 361 p. 79 illus.online resource.

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

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

1113081
Open Source.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Quantum Machine Learning with Python = Using Cirq from Google Research and IBM Qiskit /
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