Machine Learning and Quantum Devices

TL;DR

This paper introduces neural networks and deep learning techniques tailored for quantum physics applications, covering foundational methods, advanced algorithms, recent quantum applications, and potential quantum speedups in machine learning.

Contribution

It provides an accessible overview of machine learning methods and their recent quantum physics applications, highlighting the intersection of quantum computing and AI.

Findings

Neural networks can be adapted for quantum data analysis

Reinforcement learning aids in quantum control strategies

Quantum effects may accelerate machine learning processes

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.