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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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Toc

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

5.3 Linear maps

We’ve looked at linear functions several times to get a concrete idea of how they work. We must generalize this idea to vector spaces.

Let U and V be vector spaces over the same field F. Let u1 and u2 be in U and s1 and s2 be scalars in F.

The function L: UV is a linear map if

Displayed math

In particular, we have

Displayed math

When U = V, we also say L is a linear transformation of U or a linear operator on U. linear$map linear$transformation linear$operator

All linear transformations on R2 look like

Displayed math

using Cartesian coordinates, and with a, b, c, d, x, and y in R. This is interesting because the linear transformations on R1 all look like the somewhat trivial xax.

Exercise 5.2

Show that the function

Displayed math

for a, b, c, d, x, and y in C is a linear transformation of C2.

The linear transformations on R3...

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