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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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Index

Understanding quantum unitary operators

Unitary operators are defined as a unitary transformation of a rigid body rotation of the Hilbert space. When these unitary operators are applied to the basis states of the Hilbert space, for example, the and state, the results transform the state vector position but it does not change its length. Let’s see what this means for a qubit. The basis states of a qubit are mapped on the Hilbert space as described in Chapter 5, Understanding a Qubit, and , where , and are linear transformations that preserve orthogonality over unitary transformations. We’ll wrap our heads around this definition a bit by looking at this mathematically first.

A linear transformation on a complex vector space can be described by a 2x2 matrix, U:

Furthermore, if we obtain the complex transpose of the matrix U as , by transposing the matrix U and applying the complex conjugate, as illustrated:

Then we can say that the matrix U is...

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