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Machine Learning for Semiconductor Quantum Devices

01 /Supervised Learning for quantum Dots 02 /Charge Stability Diagrams 03 /Classify CS Diagrams with Neural Networks 04 /Supervised Learning with Feedback 05 /Charge Tuning as a NN with Feedback 06 /Real-Life Experimental Applications 07 /Unsupervised Learning for Quantum Dots 08 /Analyse CS Diagrams with Clustering 09 /Analyse CS Diagrams with Unsupervised NNs 10 /Autoencoders in QD Experiments 11 /ML Fine Tuning of Quantum Dots 12 /Overhauser Field estimation 13 /Recap 14 /State of the Art Tuning

Classify CS Diagrams with Neural Networks

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.

Main takeaways

  •  
  • 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.

Further thinking

True or False: Charge diagram machine learning classification has been experimentally tested in multiple labs around the world.

Solution

Further reading

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)