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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:
  • Artificial Intelligence
  • Neural and Evolutionary Computing
  • Atomic, Molecular and Optical Physics - Experiment
  • Atomic, Molecular and Optical Physics - Theory
  • Condensed Matter Physics - Experiment
  • Condensed Matter Physics - Theory
  • Quantum Physics
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.

Current status:
Editor-in-charge assigned


Submission & Refereeing History

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Submission 2101.01759v1 on 12 January 2021

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