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  • Book Overview & Buying Dancing with Qubits
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Dancing with Qubits

Dancing with Qubits - Second Edition

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 (25 chapters)
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1
I Foundations
8
II Quantum Computing
14
III Advanced Topics
18
Afterword
22
References
23
Index
1
Appendices

10.5 Eigenvalue and phase estimation

The next tool we need for Shor’s factoring algorithm is a way to estimate the eigenvalues of a special unitary operation we construct.

Let U be an n-by-n square matrix with complex entries. From section 5.10, the solutions λ of the equation algorithm$phase estimation

Displayed math

are the eigenvalues {λ1, …, λN} of U. Some of the λj may be equal. If a particular eigenvalue λj shows up k times among the N values, we say λj has multiplicity k. multiplicity

Each eigenvalue λj corresponds to an eigenvector vj so that

Displayed math

We can take each vj to be a unit vector. When U is unitary, each λj is a complex unit.

We have so far represented an eigenvalue λ of a unitary matrix as eφi with 0 ≤ φ < 2π. We now, instead, think of the eigenvalue as e2πφi with 0 ≤ φ < 1.

This change allows...

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