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

Mitigating readout errors

Ignis has measurement filters that can be used to mitigate various types of errors, such as measurements and tensors.

The measurement calibration is what we will use to mitigate measurement errors in this section. The process begins by first generating a list of circuits, where each circuit represents each of all the possible states of the qubits specified, then executing the circuits on an ideal simulator, the results of which we will then pass into a measurement filter. The measurement filter will then be used to mitigate the measurement errors. In the following example, we will first run the circuits on a simulator without any noise models. Then, we will create a noise model that will be applied to all the qubits of the simulator. Then we will execute the circuits on the noisy backend device, where we will then apply the measurement filter to mitigate the errors as best we can. Finally, we will view the results of the measurement filter and compare them...