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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

5.6 The determinant and trace

Ah, the determinant, a function on square matrices that produces values in F. It’s so elegant, so useful, tells us so much, and is such an annoying and error-prone thing to compute beyond the 2-by-2 case. matrix$determinant determinant

Let’s look at its properties before we discuss its calculation. Let A be an n-by-n matrix. We denote its determinant by det(A):

  • det(A) ≠ 0 if and only if A is invertible.
  • For b a scalar in F, det(bA) = bn det(A).
  • If any row or column of A is all zeros, then det(A) = 0. The determinant being zero does not imply a row or column is zero.
  • If A is upper or lower triangular, the determinant is the product of the diagonal entries. If one of those diagonal entries is 0, the determinant is thus 0.
  • In particular, det(I) = 1 for I an identity matrix.
  • The determinant behaves well when taking transposes and conjugates:
...
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