You're reading from Quantum Computing with Silq Programming Get up and running with quantum computing with the simplicity of this new high-level programming language

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800569669

Length 310 pages

Edition 1st Edition

Languages

C++

Tools

Quantum Development Kit

Concepts

Programming Language

Authors (2):

Thomas Cambier

Srinjoy Ganguly

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: Essential Background and Introduction to Quantum Computing

2. Chapter 1: Essential Mathematics and Algorithmic Thinking FREE CHAPTER

3. Chapter 2: Quantum Bits, Quantum Measurements, and Quantum Logic Gates

4. Chapter 3: Multiple Quantum Bits, Entanglement, and Quantum Circuits

5. Chapter 4: Physical Realization of a Quantum Computer

6. Section 2: Challenges in Quantum Programming and Silq Programming

7. Chapter 5: Challenges in Quantum Computer Programming

8. Chapter 6: Silq Programming Basics and Features

9. Chapter 7: Programming Multiple-Qubit Quantum Circuits with Silq

10. Section 3: Quantum Algorithms Using Silq Programming

11. Chapter 8: Quantum Algorithms I – Deutsch-Jozsa and Bernstein-Vazirani

12. Chapter 9: Quantum Algorithms II – Grover's Search Algorithm and Simon's Algorithm

13. Chapter 10: Quantum Algorithms III – Quantum Fourier Transform and Phase Estimation

14. Section 4: Applications of Quantum Computing

15. Chapter 11: Quantum Error Correction

16. Chapter 12: Quantum Cryptography – Quantum Key Distribution

17. Chapter 13: Quantum Machine Learning

18. Other Books You May Enjoy

Getting started with quantum machine learning

In the previous section, you learned about the field of machine learning and the three most common algorithms used by machine learning researchers and by industries to accelerate growth. Every year, we are now witnessing a significant amount of growth of data worldwide, and the rate is going to increase even more in the near future. It has been estimated that about 60 zetabytes () has been accumulated by our planet!

Due to the growth of data and its associated features, the power of classical computers is becoming limited. We have powerful classical computers today that can deal with high-dimensional data, but a time is coming when classical computers won't be able to handle very high-dimensional data, and at that time quantum machine learning algorithms will have to step in.

The term quantum machine learning was coined by Lloyd, Mohseni, and Rebentrost in their paper Quantum algorithms for supervised and unsupervised machine...