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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

13.6 Summary

This chapter introduced two quantum alternatives to classical machine learning methods: quantum neural networks and kernels. We also looked at several ways to represent classical data in quantum states.

Throughout the discussions, we returned to the same question several times: Can we show that quantum machine learning offers a significant quantum advantage compared to classical methods? The answer is a definite “maybe … one day … possibly”! In any case, “not yet” is the consensus at the time of writing. I encourage you to track the progress of quantum machine learning as quantum computers increase in size, performance, and quality. To avoid the hype, you should keep an open mind but always ask yourself honest questions about what you are trying to accomplish with what tools. 232

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