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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

Unsupervised Learning for Quantum Dots

In this video we motivate the concept of unsupervised learning for quantum dot control and tuning tasks, and outline two main topics we will discuss in this model: clustering and generative models

Main takeaways

  • Unsupervised learning is key in any circumstances where we do not have access to labelled data (or the labelling process is too costly)
  • Many unsupervised learning tasks can be accomplished without neural networks via clustering algorithms
  • An example of unsupervised learning with neural networks is an Autoencoder

Further thinking

True or False: Unsupervised learning is a sub-category of supervised learning.

Solution

Further reading

More About Clustering - PCA and more: https://ml-lectures.org/docs/structuring_data/ml_without_neural_network.html

Kernel principal component analysis: https://link.springer.com/chapter/10.1007/BFb0020217

More about Unsupervised Learning - Autoencoders and more: https://ml-lectures.org/docs/unsupervised_learning/ml_unsupervised.html

Reducing the Dimensionality of Data with Neural Networks: https://www.science.org/doi/10.1126/science.1127647