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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

13.3 Quantum neural networks

Let’s recall some definitions regarding neural networks from my book Dancing with Python. 211, Section 15.8 neural network quantum$neural network node neuron

Figure 13.3 shows a neural network with three input nodes, four nodes in the hidden layer, and two output nodes. Another name for a node is a neuron. I’ve shown weights w in the network on the connections from the input nodes going to the hidden nodes, and from the hidden nodes to the output nodes. Note how the network sends the value of each node to every node in the next layer.

 Figure 13.3: Neural network with 3 inputs, 4 hidden nodes, and 2 outputs

We compute a value from the input values and weights for each node in the hidden layers. These are real numbers that we may restrict to the binary values 0 and 1. We also have an associated activation function for each node in the hidden layer, determining what value to send to...

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