Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Dancing with Qubits
  • Toc
  • feedback
Dancing with Qubits

Dancing with Qubits

By : Robert S. Sutor
5 (24)
close
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)
close
1
I Foundations
8
II Quantum Computing
14
III Advanced Topics
18
Afterword
22
Other Books You May Enjoy
23
References
24
Index
Appendices

6.8 Markov and Chebyshev go to the casino

In this section, we work through the probability math of estimating π as we previously explored in section 1.5. We dropped coins into a square and looked at how many of them had their centers on or inside a circle. Chebyshev’s Inequality Markov, Andrey Chebyshev, Pafnuty

There are two important inequalities involving expected values, variances, and error terms. Let X be a finite random variable with a known distribution so that

Displayed math

and each xk ≥ 0.

Markov’s Inequality

For a real number a > 0, Markov’s Inequality

Displayed math

In Markov’s Inequality, the expression P(X > a) means “look at all the xk in X, and for all those where xk > a, add up the pk to get P(X > a).”

Exercise 6.8

Show that Markov’s Inequality holds for the distribution at the beginning of section 6.6 for...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete