Machine Learning and Quantum Devices

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.

48 pages, 7 figures. Submitted to SciPost Lecture Notes. To appear in 'Quantum Information Machines; Lecture Notes of the Les Houches Summer School 2019', eds. M. Devoret, B. Huard, and I. Pop. This is version 2 with the very few changes suggested by the referee

References in corpus (3)

• A Brief Survey of Deep Reinforcement Learning
• An introduction to quantum machine learning
• Quantum-enhanced machine learning

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• Deep Reinforcement Learning for Radiative Heat Transfer Optimization Problems
• Gaussian boson sampling and multi-particle event optimization by machine learning in the quantum phase space
• A machine learning based approach to the identification of spectral densities in quantum open systems
• Discovering Local Hidden-Variable Models for Arbitrary Multipartite Entangled States and Arbitrary Measurements
• Variational quantum algorithm for Gaussian discrete solitons and their boson sampling
• Exploring Fresnel diffraction at a straight edge with a neural network
• Error mitigation of entangled states using brainbox quantum autoencoders