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DelftX: Machine Learning for Semiconductor Quantum Devices

Learn how to deploy artificial intelligence to control and calibrate semiconductor quantum computing chips

Machine Learning for Semiconductor Quantum Devices
6 weeks
6–7 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

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Starts Apr 12
Ends Jul 31

About this course

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Quantum computing is a fast-growing technology and semiconductor chips are one of the most promising platforms for quantum devices.
The current bottleneck for scaling is the ability to control semiconductor computing chips quickly and efficiently.

This course, aimed at students with experience equivalent to a master’s degree in physics, computer science or electrical engineering introduces hands-on machine learning examples for the application of machine learning in the field of semiconductor quantum devices. Examples include coarse tuning into the correct quantum dot regime, specific charge state tuning, fine tuning and unsupervised quantum dot data analysis.

After the completion of the course students will be able to

  1. assess the suitability of machine learning for specific qubit tuning or control task and
  2. implement a machine learning prototype that is ready to be embedded into their experimental or theoretical quantum research and engineering workflow.

At a glance

What you'll learn

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  1. To understand the utility of machine learning in tuning of semiconductor quantum devices
  2. To formulate various stages of tuning as a machine learning problem
  3. To develop and implement in Python a machine learning prototype for variety of semiconductor qubit tuning tasks
  4. To assess the suitability of machine learning in specific semiconductor quantum computing experimental workflows

Week 0: Introduction to the course and self-study of the prerequisites

Week 1: Supervised learning for quantum dot configuration tuning

  • Review of neural networks
  • Formulate configuration tuning as a neural network learning task
  • Applicability for quantum experiments
  • Coding demonstration: Supervised supervised neural network configuration classification

Week 2: Charge tuning with neural networks

  • Introduction to charge tuning
  • Tuning to specific charge states as supervised neural network with feedback loop
  • Experimental charge tuning
  • Coding demonstration: Charge charge state preparation using neural network with feedback loop
  • Midterm exam (multiple choice)

Week 3: Unsupervised learning for analysis of quantum dot data

  • Introduction to unsupervised learning
  • Clustering methods for analysis of charge stability diagrams
  • Outlook and applicability to experimental systems
  • Coding demonstration: kernel-PCA clustering of charge stability data

Week 4: Fine-tuning with neural networks

  • Introduction to fine-tuning
  • Fine Fine-tuning as a Hamiltonian learning problem
  • Experimental fine-tuning
  • Coding demonstration: Hamiltonian learning for qubit characterization

Week 5: Conclusion and Recap

  • Overview of the techniques and applications
  • Outlook for artificial intelligence as a tool for control and calibration of quantum devices
  • Final exam - multiple choice and optional project (video brief) with a forum for questions

Frequently Asked Questions

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I am a machine learning expert, but don’t know much about semiconductor qubits. Can I still take this course?
Yes, but we recommended that you first follow
https://www.qutube.nl/courses-10/quantum-computer-12/quantum-dot-qubits-336

I am a quantum device expert, but don’t know much about machine learning. Can I still take this course?
Yes, but we recommend spending Week 0 following the recommended pre-requisites closely to get the maximum benefit from the course.

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

This course is part of Quantum 301: Quantum Computing with Semiconductor Technology Professional Certificate Program

Learn more 
Expert instruction
2 skill-building courses
Self-paced
Progress at your own speed
3 months
6 - 7 hours per week

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