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Learn Quantum Computing with Python and IBM Quantum

Learn Quantum Computing with Python and IBM Quantum

By : Robert Loredo
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Learn Quantum Computing with Python and IBM Quantum

Learn Quantum Computing with Python and IBM Quantum

By: Robert Loredo

Overview of this book

IBM Quantum Lab 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 on several real quantum computers. Updated with new examples and changes to the platform, this edition begins with an introduction to the IBM Quantum dashboard and Quantum Information Science Kit (Qiskit) SDK. You will become well versed with the IBM Quantum Composer interface as well as the IBM Quantum Lab. You will learn the differences between the various available quantum computers and simulators. Along the way, you’ll learn some of the fundamental principles regarding quantum mechanics, quantum circuits, qubits, and the gates that are used to perform operations on qubits. As you build on your knowledge, you’ll understand the functionality of IBM Quantum and the developer-focused resources it offers to address key concerns like noise and decoherence within a quantum system. You’ll learn how to monitor and optimize your quantum circuits. Lastly, you’ll look at the fundamental quantum algorithms and understand how they can be applied effectively. By the end of this quantum computing book, you'll know how to build quantum programs and will have gained a practical understanding of quantum computation that you can apply to your business.
Table of Contents (18 chapters)
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14
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15
Index

Summary

In this chapter, you learned about the IBM Quantum Composer and its many components. You created an experiment that simulated a classic NOT gate. You then viewed the results on a histogram, and read the probabilities based on the results.

This has provided you with the skills to experiment with other gates to see what effect each operation has on each qubit and what information might be determined or used based on the results of the operation. This will be helpful when we look at some of the quantum algorithms and how these operations are leveraged to solve certain problems.

In the next set of chapters, we will move away from the click-and-drag work of the UI and instead create experiments using Jupyter Notebook, as well as beginning to program quantum circuits using Python.

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