Machine learning and quantum devices
Florian Marquardt
SciPost Phys. Lect. Notes 29 (2021) · published 31 May 2021
Part of the 2019-07: Quantum Information Machines Collection in the Les Houches Summer School Lecture Notes Series.
- doi: 10.21468/SciPostPhysLectNotes.29
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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.
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