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Dancing with Qubits

Dancing with Qubits

By : Robert S. Sutor
5 (24)
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Dancing with Qubits

Dancing with Qubits

5 (24)
By: Robert S. Sutor

Overview of this book

Dancing with Qubits, Second Edition, is a comprehensive quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. A full description of classical computing and the mathematical underpinnings of quantum computing follows, helping you better understand concepts such as superposition, entanglement, and interference. Next up are circuits and algorithms, both basic and sophisticated, as well as a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments may affect you. This new edition is updated throughout with more than 100 new exercises and includes new chapters on NISQ algorithms and quantum machine learning. Understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is explained thoroughly and with helpful examples, leaving you with a solid foundation of knowledge in quantum computing that will help you pursue and leverage quantum-led technologies.
Table of Contents (26 chapters)
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1
I Foundations
8
II Quantum Computing
14
III Advanced Topics
18
Afterword
22
Other Books You May Enjoy
23
References
24
Index
Appendices

Introduction to Quantum Machine Learning

Learning is the only thing the mind never exhausts, never fears, and never regrets.

Leonardo da Vinci

It’s hard to imagine an area of computer science and data analysis that has gotten more attention and investment in recent years than AI and machine learning. While quantum computers are still not “big data” machines because of their relatively short coherence times and numbers of qubits, it is still reasonable to ask if we can extend or replace existing machine learning algorithms or computational components with quantum versions. This is the field of quantum machine learning, or QML. machine learning machine learning$quantum quantum$machine learning QML

This chapter surveys and summarizes several techniques where quantum computing might improve the performance or accuracy of neural networks and support vector machines for classification. The chapter builds on the discussion in section 1.4. algorithm$support...

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