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

Creating a neural network discriminator

Machine learning is a growing area in quantum computing. Researchers have been looking at ways to leverage quantum computational techniques in various areas of machine learning, such as generative adversarial networks and supervised learning for regression and classification.

In this section, we will focus on the discriminator model as opposed to the generative model. As a reminder, the discriminator model learns by using conditional probability distribution, whereas the generative model learns via joint probability distribution.

In this section, we will create a PyTorchDiscriminator class based on PyTorch. This class contains various methods that will allow you to load your model and perform a training step, based on the parameters of your discriminator. Let's get started:

  1. First, we'll create a PyTorchDiscriminator class by specifying the number of features (the dimension of the input data vector) and the dimension of...