Book Image

Quantum Computing in Practice with Qiskit® and IBM Quantum Experience®

By : Hassi Norlen
5 (1)
Book Image

Quantum Computing in Practice with Qiskit® and IBM Quantum Experience®

5 (1)
By: Hassi Norlen

Overview of this book

IBM Quantum Experience® is a leading platform for programming quantum computers and implementing quantum solutions directly on the cloud. This book will help you get up to speed with programming quantum computers and provide solutions to the most common problems and challenges. You’ll start with a high-level overview of IBM Quantum Experience® and Qiskit®, where you will perform the installation while writing some basic quantum programs. This introduction puts less emphasis on the theoretical framework and more emphasis on recent developments such as Shor’s algorithm and Grover’s algorithm. Next, you’ll delve into Qiskit®, a quantum information science toolkit, and its constituent packages such as Terra, Aer, Ignis, and Aqua. You’ll cover these packages in detail, exploring their benefits and use cases. Later, you’ll discover various quantum gates that Qiskit® offers and even deconstruct a quantum program with their help, before going on to compare Noisy Intermediate-Scale Quantum (NISQ) and Universal Fault-Tolerant quantum computing using simulators and actual hardware. Finally, you’ll explore quantum algorithms and understand how they differ from classical algorithms, along with learning how to use pre-packaged algorithms in Qiskit® Aqua. By the end of this quantum computing book, you’ll be able to build and execute your own quantum programs using IBM Quantum Experience® and Qiskit® with Python.
Table of Contents (12 chapters)

Adding noise profiles of IBM Quantum® backends to local simulators

In this recipe, we find the noise data for the IBM Quantum® backends to build a noise profile that we can then add to our simulator when we run it. This will make the simulator behave like a real NISQ backend.

Getting ready

The sample code for this recipe can be found here:

How to do it...

Let's look at the following code:

  1. Get a list of the available backends and select one to simulate.

    We will get the noise profile of one of the IBM Quantum® backends and use it with our simulators. First, we use the select_backend() function to list the backends and make the selection:

    def select_backend():
        # Get all available and operational backends.
        available_backends = provider.backends(filters=lambda ...