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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

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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