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Dancing with Qubits

Dancing with Qubits

By : Robert S. Sutor
5 (24)
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Dancing with Qubits

Dancing with Qubits

5 (24)
By: Robert S. Sutor

Overview of this book

Dancing with Qubits, Second Edition, is a comprehensive quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. A full description of classical computing and the mathematical underpinnings of quantum computing follows, helping you better understand concepts such as superposition, entanglement, and interference. Next up are circuits and algorithms, both basic and sophisticated, as well as a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments may affect you. This new edition is updated throughout with more than 100 new exercises and includes new chapters on NISQ algorithms and quantum machine learning. Understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is explained thoroughly and with helpful examples, leaving you with a solid foundation of knowledge in quantum computing that will help you pursue and leverage quantum-led technologies.
Table of Contents (26 chapters)
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1
I Foundations
8
II Quantum Computing
14
III Advanced Topics
18
Afterword
22
Other Books You May Enjoy
23
References
24
Index
Appendices

12.1 Cost functions and optimization

Let’s begin with an ad hoc definition of a concept we need when discussing many NISQ algorithms, the cost function, where we defined functions in section 4.1. As you would expect, such a function C computes the cost of something given one or more inputs. We might express the cost in money, work hours, resources consumed, relative health while undergoing a new medical treatment, or any measure of something used or lost. In machine learning, we employ a cost function to measure how close actual data values are to those predicted by a model we construct. optimization function$cost cost$function

In all these examples, we are interested in minimizing the cost function. We want to find when something costs the least money, requires the fewest hours of work or resources, causes the most minor medical trauma or inconvenience, or produces an AI model closest to the training data. This is an optimization problem.

The examples in...

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