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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

Autoencoders in QD Experiments

In this video we provide an overview of the contemporary experimental uses of autoencoders.

Main takeaways

  • Autoencoders can help score measurement points in a parameter space to help select measurements with a maximal informational yield.

Further thinking

True or False: All points in charge stability diagrams are equally useful when we measure current changes as a function of voltages.

Solution

Further reading

Discovering Phase Transitions with Unsupervised Learning: https://arxiv.org/pdf/1606.00318.pdf

Paper: Quantum device fine-tuning using unsupervised embedding learning: https://iopscience.iop.org/article/10.1088/1367-2630/abb64c

Efficiently measuring a quantum device using machine learning: https://www.nature.com/articles/s41534-019-0193-4