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About Machine Learning and Quantum Devices: Florian Marquardt April 22, 2020
This document provides an introduction to machine learning and neural networks for physicists. It begins with an overview of artificial neural networks and their applications in science and technology, including for interpreting data in physics. The document then discusses the basic structure and training of neural networks using backpropagation and stochastic gradient descent. It provides examples of using neural networks for function approximation, image classification, and state reconstruction in quantum physics. Later sections cover more advanced techniques like reinforcement learning, Boltzmann machines, and recurrent neural networks. The final sections discuss applications of machine learning to quantum devices and the potential for quantum computing to accelerate machine learning.
Detailed Information
| Author: | ['Hasitha m'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 40 |
| Language: | English |
| Format: | |
| Price: | FREE |
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