In this video, we show how to view charge stability diagram classification as a routine machine-learning image processing task. We show how to classify charge stability diagrams using dense and convolutional neural networks. We also discuss the broader usage of these techniques in contemporary semiconductor qubit experiments.
- Distinguishing single and double quantum dot charge stability diagram maps straightforwardly on the known image classification problem
- Classification can be achieved with the fully connected neural network, but convolutional neural networks are a better tool in this case since we are parsing the charge stability diagrams for a specific feature.
True or False: Charge diagram machine learning classification has been experimentally tested in multiple labs around the world.
A nice introduction to supervised learning with neural networks: https://ml-lectures.org/docs/supervised_learning_w_NNs/ml_supervised_w_NNs.html
Online book for Neural Networks and Deep Learning: http://neuralnetworksanddeeplearning.com/
Experimental paper that inspired the classification exercise we performed in this course: https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.13.054005 (Arxiv: https://arxiv.org/abs/1911.10709)