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)

Comparing backends

The IBM Quantum® backends are all slightly different, from the number of qubits to the individual behavior and interaction between these. Depending on how you write your quantum program, you might want to run the code on a machine with certain characteristics.

The backend information that is returned by IBMQ is just a plain Python list and you can juggle the returned data with any other list. For example, you can write a Python script that finds the available IBM Quantum® backends, then run a quantum program on each of the backends and compare the results in a diagram that shows a rough measure of the quality of the backends' qubits.

In this recipe, we will use a simple Python loop to run a succession of identical Bell-state quantum programs on the available IBM Quantum® backends to get a rough estimate of the performance of the backends.

Getting ready

The file required for this recipe can be downloaded from here: https://github.com...