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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

13.1 What is machine learning?

Consider my complete product browsing and purchase history with an online retailer. This data represents my experience of buying from the seller and their experience selling to me. How can the data and my new purchases help the retailer learn how to recommend and sell me additional products and services?

Machine learning is a large set of techniques where computer algorithms learn from existing data rather than having hardcoded decisions built into them. We create a model and train it from a subset of our data. If we have done a good job, this model predicts what we should do next to accomplish a goal, such as selling someone new clothes, books, or music. model

Such algorithms and models improve their accuracy as they receive new data. They might also improve when humans or processes intervene to judge the correctness and quality of predictions and results.

In unsupervised learning, an algorithm looks for patterns in the data...

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