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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

2.6 Algorithmically speaking

We often use the word “algorithm” generically to mean “something a computer does.” Quantitative analysts employ financial market algorithms to calculate the exact moment and price to sell a stock or bond. They are used in artificial intelligence to find patterns in data to understand natural language, construct responses in human conversation, find manufacturing anomalies, detect financial fraud, and even create new spice mixtures for cooking.

Informally, an algorithm is a recipe. Like a food recipe, an algorithm states what inputs you need (water, flour, butter, eggs, etc.), the expected outcome (for example, bread), the sequence of steps you take, the subprocesses you should use (stir, knead, bake, cool), and what to do when a choice presents itself (“if the dough is too wet, add more flour”).

We call each step an operation and give it a name as above: “stir,” “bake,&...

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