Book Image

Learn Quantum Computing with Python and IBM Quantum Experience

By : Robert Loredo
Book Image

Learn Quantum Computing with Python and IBM Quantum Experience

By: Robert Loredo

Overview of this book

IBM Quantum Experience is a platform that enables developers to learn the basics of quantum computing by allowing them to run experiments on a quantum computing simulator and a real quantum computer. This book will explain the basic principles of quantum mechanics, the principles involved in quantum computing, and the implementation of quantum algorithms and experiments on IBM's quantum processors. You will start working with simple programs that illustrate quantum computing principles and slowly work your way up to more complex programs and algorithms that leverage quantum computing. As you build on your knowledge, you’ll understand the functionality of IBM Quantum Experience and the various resources it offers. Furthermore, you’ll not only learn the differences between the various quantum computers but also the various simulators available. Later, you’ll explore the basics of quantum computing, quantum volume, and a few basic algorithms, all while optimally using the resources available on IBM Quantum Experience. By the end of this book, you'll learn how to build quantum programs on your own and have gained practical quantum computing skills that you can apply to your business.
Table of Contents (21 chapters)
1
Section 1: Tour of the IBM Quantum Experience (QX)
5
Section 2: Basics of Quantum Computing
9
Section 3: Algorithms, Noise, and Other Strange Things in Quantum World
18
Assessments
Appendix A: Resources

Chapter 14: Applying Quantum Algorithms

In this chapter, we will focus on algorithms that have the potential to solve more applicable problems, such as periodicity and searching. These algorithms differ from the earlier algorithms as these are used in various domains and are included in many modern quantum algorithms. A few examples of these modern quantum algorithms are the quantum amplitude estimation, variational quantum Eigensolvers, and quantum support vector machines algorithms. Having a good understanding of these will help you when learning about or creating your own algorithms as the techniques used can be applied in many industries.

Period algorithms can be used to solve factorization or phase estimation problems. Search algorithms can also provide some speed-up over classical algorithms on how they leverage amplitude amplification to find a specified entry.

The following topics will be covered in this chapter:

  • Understanding periodic quantum algorithms
  • Learning...