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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

12.10 Summary

In this chapter, we looked at QAOA as an example of a quantum technique that does not require fault-tolerant, error-corrected qubits. QAOA is an example of a variational quantum eigensolver that uses the variational principle from physics to find solutions to combinatorial optimization problems, such as Max-Cut.

Variational algorithms have potential value because they can use relatively short-depth circuits in conjunction with classical optimization techniques, such as gradient descent. We introduced the concepts of Hamiltonian and ansatz and showed how expectation allows us to compute an upper bound for eigenvalues.

The chapter concluded with a sober look at the perceived versus actual value of NISQ algorithms. Do you think we should bother, or should we focus our efforts on fault-tolerant, error-corrected qubits?

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