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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

Supervised Learning for quantum Dots

In this video, we motivate why machine learning algorithms are beneficial in tuning and control task of quantum devices - and specifically, those based on semiconductor technology. We cover the concept of tuning as a high-level optimization problem and show how to conceptualize it as an input-output machine learning problem.

Main takeaways

  •  
  • Any tuning problem can be understood as a high-dimensional optimization
  • There are distinct levels of abstraction in tuning:
    1. directly parsing the structure in measured data
    2. mapping the measured data on physics parameters
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  • Solid-state qubits are unique candidates for ML techniques: inherent properties of semiconductor qubits present time-dependent complex noise that requires generalisation and more sophisticated data processing

Further thinking

True or False:Semiconductor qubits are easier to fabricate than superconducting qubits and therefore they are easier to tune.

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)