Title
Quantum Machine Learning with Python: Using Cirq from Google Research and IBM Qiskit,Used
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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 subatomic 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 (HarrowHassidimLloyd) among others.You'll then be introduced to Quantum machine learning and Quantum deep learningbased 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 LearnUnderstand 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 ForMachine Learning enthusiasts and engineers who want to quickly scale up to Quantum Machine Learning
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- Q: What topics are covered in 'Quantum Machine Learning with Python'? A: The book covers fundamental concepts of Quantum Computing, including Dirac Notations, Qubits, and Bell states. It also explores Quantum machine learning applications, Quantum algorithms like Quantum Fourier transform, phase estimation, and HHL, as well as advanced topics such as Quantum adiabatic processes and optimization.
- Q: Who is the author of this book? A: The author of 'Quantum Machine Learning with Python' is Santanu Pattanayak.
- Q: What is the format of this book? A: The book is available in paperback format.
- Q: How many pages does the book have? A: The book contains 384 pages.
- Q: Is this book suitable for beginners in Quantum Computing? A: Yes, the book is designed for Machine Learning enthusiasts and engineers who want to quickly scale up to Quantum Machine Learning, making it suitable for beginners.
- Q: What programming languages are used in the book? A: The book includes Python implementations of various Quantum machine learning and Quantum computing algorithms, specifically using the Qiskit toolkit from IBM and Cirq from Google Research.
- Q: When was 'Quantum Machine Learning with Python' published? A: The book was published on March 13, 2021.
- Q: What can readers expect to learn from this book? A: Readers can expect to understand Quantum Computing and machine learning, develop expertise in algorithm development, and explore the challenges of building large-scale Quantum computers.
- Q: Is this book focused on theory or practical applications? A: The book balances both theory and practical applications, providing insights into Quantum algorithms and real-world use cases in various fields.
- Q: What edition of the book is available? A: The book is available in its first edition.