SciPost Submission Page
Machine Learning and Quantum Devices
by Florian Marquardt
Submission summary
As Contributors: | Florian Marquardt |
Arxiv Link: | https://arxiv.org/abs/2101.01759v1 (pdf) |
Code repository: | https://github.com/FlorianMarquardt/machine-learning-for-physicists |
Date submitted: | 2021-01-12 14:02 |
Submitted by: | Marquardt, Florian |
Submitted to: | SciPost Physics Lecture Notes |
Academic field: | Physics |
Specialties: |
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Approaches: | Theoretical, Experimental, Computational |
Abstract
These brief lecture notes cover the basics of neural networks and deep learning as well as their applications in the quantum domain, for physicists without prior knowledge. In the first part, we describe training using backpropagation, image classification, convolutional networks and autoencoders. The second part is about advanced techniques like reinforcement learning (for discovering control strategies), recurrent neural networks (for analyzing time traces), and Boltzmann machines (for learning probability distributions). In the third lecture, we discuss first recent applications to quantum physics, with an emphasis on quantum information processing machines. Finally, the fourth lecture is devoted to the promise of using quantum effects to accelerate machine learning.