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Quantum Machine Learning With Python: Using Cirq From Google Research And Ibm Qiskit
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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: How many pages does this book have? A: The book contains three hundred eighty-four pages. This length provides a comprehensive overview of quantum machine learning and its applications.
- Q: What is the binding type of this book? A: This book is available in paperback binding. This makes it lightweight and easy to handle for reading and note-taking.
- Q: What are the dimensions of the book? A: The book measures seven point zero one inches in length, zero point eight seven inches in width, and ten inches in height. These dimensions make it a standard size for books on technical subjects.
- Q: How do I get started with quantum machine learning? A: To start, read the foundational chapters that cover quantum computing concepts. The book provides practical examples and Python implementations using Qiskit and Cirq.
- Q: Is this book suitable for beginners in quantum computing? A: Yes, this book is suitable for beginners. It begins with fundamental concepts and gradually introduces more complex topics and algorithms.
- Q: Can I use this book for self-study? A: Yes, this book is great for self-study. It includes explanations, examples, and exercises that allow readers to learn at their own pace.
- Q: How should I store this book to keep it in good condition? A: Store the book in a cool, dry place, away from direct sunlight. Keeping it on a bookshelf upright will help prevent damage.
- Q: Are there any safety concerns with this book? A: No, there are no safety concerns with this book. It is a standard paperback book without hazardous materials or adult content.
- Q: What if my book arrives damaged? A: If your book arrives damaged, you should contact the seller for a return or replacement. Most sellers have return policies for damaged items.
- Q: Can I return this book if I don't like it? A: Yes, you can typically return the book within a specified period. Check the seller's return policy for specific details.
- Q: Is this book suitable for advanced learners? A: Yes, advanced learners will find value in this book. It covers complex quantum algorithms and topics, making it a useful resource.
- Q: What topics are covered in this book? A: The book covers quantum computing, quantum machine learning, and various algorithms like the Quantum Fourier transform. It's comprehensive in its approach to the subject.
- Q: Does this book include practical coding examples? A: Yes, the book includes practical coding examples using Python. It specifically utilizes Qiskit from IBM and Cirq from Google Research.
- Q: Who is the author of this book? A: The author of the book is Santanu Pattanayak. He has expertise in quantum machine learning and related fields.
- Q: What genre does this book fall under? A: This book falls under the genre of Intelligence and Semantics. It focuses on advanced topics in quantum computing and machine learning.