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Dancing with Qubits

You're reading from   Dancing with Qubits From qubits to algorithms, embark on the quantum computing journey shaping our future

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781837636754
Length 684 pages
Edition 2nd Edition
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Author (1):
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Robert S. Sutor Robert S. Sutor
Author Profile Icon Robert S. Sutor
Robert S. Sutor
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Table of Contents (26) Chapters Close

Preface I Foundations
Why Quantum Computing FREE CHAPTER They’re Not Old, They’re Classics More Numbers Than You Can Imagine Planes and Circles and Spheres, Oh My Dimensions 6 What Do You Mean “Probably”? II Quantum Computing
One Qubit Two Qubits, Three Wiring Up the Circuits From Circuits to Algorithms Getting Physical III Advanced Topics
Considering NISQ Algorithms Introduction to Quantum Machine Learning Questions about the Future Afterword
A Quick Reference B Notices C Production Notes Other Books You May Enjoy
References
Index
Appendices

5.10 Eigenvectors and eigenvalues

Let’s review some of the features of diagonal matrices. Recall that a diagonal matrix has 0s everywhere except maybe on the main diagonal. A simple example for R3 is matrix$diagonal matrix$eigenvalue eigenvalue matrix$eigenvector eigenvector

Displayed math

Its effect on the standard basis vectors e1, e2, and e3 is to stretch by a factor of 3 along the first, leave the second alone, reflect across the xy-plane, and then stretch by a factor of 2 along the third.

A general diagonal matrix looks like

Displayed math

Of course, we might be dealing with a small matrix and not have quite so many zeros. Some of the dj might be zero.

For a diagonal matrix D as above,

  • det(D) = d1 d2dn.
  • tr (D) = d1 + d2 + ⋯ + dn.
  • DT = D.
  • D is invertible if and only if none of the dj are 0.
  • If D is invertible,
Displayed math
  • If {b1, b2, …...
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