Quantum Hardware and its Applications with Quantum Inspire
Intermediate
24-30 H
Quantum Applications
Quantum Applications
Machine Learning for Semiconductor Quantum Devices
Level
Advanced
Modules
6 Modules
Duration
30-35 H
Instructor
E. Greplova, A. Chatterjee, A. Ahmadi, T. Tanev, V. F. Hernandes, G. Jin, T. Rasmussen, G. Bastasin, P. M. May
Course info
Dear learners,
Welcome to the first part of the Program Quantum 301: Quantum Computing with Semiconductor Technology.
In this MOOC, you will learn to implement machine learning techniques to analyze results, automate tuning routines, and improve semiconductor quantum device simulations, focusing on existing and novel applications of semiconductor-based technologies.
Among the different semiconductor quantum devices presented here, the next MOOC will zoom in on germanium-based quantum devices with their latest developments and applications.
Please note: this is an advanced course. We expect you to have followed the previous courses offered by QuTech Academy, namely:
- Fundamentals of Quantum Information
- The Hardware of a Quantum Computer
- Architecture, Algorithms, and Protocols of a Quantum Computer and Quantum Internet
If you have already followed some other courses about Quantum Computing, you can check the Syllabus and make sure that your prior knowledge is sufficient to follow the course. If you don’t feel confident about some of the topics, join the previous courses and come back later!
The course is a journey of discovery, so we encourage you to bring your own experiences, insights and thoughts via the forum. Our Community TA’s will be available during working days to support you. We aim to answer all your questions within 48 hours.
We hope you enjoy the course with us!
Kind Regards,
The Course Team
1.
Module 1: Tuning with Supervised Neural Networks
Intro
Log in to access module Intro1.1 Machine Learning for Tuning Quantum Devices
Log in to access module 1.1 Machine Learning for Tuning Quantum Devices1.1 Quiz
Log in to access module 1.1 Quiz1.2 Neural Networks Primer
Log in to access module 1.2 Neural Networks Primer1.3.1 Introduction to Charge Stability Diagrams
Log in to access module 1.3.1 Introduction to Charge Stability Diagrams1.3.1 Quiz
Log in to access module 1.3.1 Quiz1.3.2 Neural Network Charge Stability Diagram Classification
Log in to access module 1.3.2 Neural Network Charge Stability Diagram Classification1.3.2 Quiz
Log in to access module 1.3.2 Quiz1.3.3 Code Demo – Module 1
Log in to access module 1.3.3 Code Demo – Module 11.3.3 Quiz
Log in to access module 1.3.3 Quiz1.4 Additional Reading
Log in to access module 1.4 Additional ReadingForum – Module 1
Log in to access module Forum – Module 1
2.
Module 2: Charge State Tuning with Neural Networks
Intro
Log in to access module Intro2.1.1 Charge Tuning Problem
Log in to access module 2.1.1 Charge Tuning Problem2.1.1 Quiz
Log in to access module 2.1.1 Quiz2.1.2 Charge Tuning as a Neural Net with Feedback
Log in to access module 2.1.2 Charge Tuning as a Neural Net with Feedback2.1.2 Quiz
Log in to access module 2.1.2 Quiz2.1.3 Experimental Usage
Log in to access module 2.1.3 Experimental Usage2.1.3 Quiz
Log in to access module 2.1.3 Quiz2.1.4 Code Demo – Module 2
Log in to access module 2.1.4 Code Demo – Module 22.1.4 Quiz
Log in to access module 2.1.4 Quiz2.2 Additional Reading
Log in to access module 2.2 Additional ReadingForum – Module 2
Log in to access module Forum – Module 2
3.
Midterm Exam
4.
Module 3: Unsupervised Learning for Quantum Dots Data
Intro
Log in to access module Intro3.1.1 Introduction to Unsupervised Learning
Log in to access module 3.1.1 Introduction to Unsupervised Learning3.1.1 Quiz
Log in to access module 3.1.1 Quiz3.1.2 Analyze Charge Stability Diagrams with Clustering Techniques
Log in to access module 3.1.2 Analyze Charge Stability Diagrams with Clustering Techniques3.1.2 Quiz
Log in to access module 3.1.2 Quiz3.1.3 Principal Component Analysis Math and Extension to Kernel PCA
Log in to access module 3.1.3 Principal Component Analysis Math and Extension to Kernel PCA3.1.3 Quiz
Log in to access module 3.1.3 Quiz3.1.4 CS Diagram Analysis with Unsupervised Neural Networks
Log in to access module 3.1.4 CS Diagram Analysis with Unsupervised Neural Networks3.1.4 Quiz
Log in to access module 3.1.4 Quiz3.1.5 Autoencoders in Contemporary Quantum Dot Experiments
Log in to access module 3.1.5 Autoencoders in Contemporary Quantum Dot Experiments3.1.5 Quiz
Log in to access module 3.1.5 Quiz3.1.6 Code Demo – Module 3
Log in to access module 3.1.6 Code Demo – Module 33.1.6 Quiz
Log in to access module 3.1.6 Quiz3.2 Additional Reading
Log in to access module 3.2 Additional ReadingForum – Module 3
Log in to access module Forum – Module 3
5.
Module 4: Machine Learning for Fine-Tuning of Quantum Dots
Intro
Log in to access module Intro4.1.1 Introduction to Fine Tuning in Quantum Dots
Log in to access module 4.1.1 Introduction to Fine Tuning in Quantum Dots4.1.1 Quiz
Log in to access module 4.1.1 Quiz4.1.2 Overhauser Field Estimation
Log in to access module 4.1.2 Overhauser Field Estimation4.1.2 Quiz
Log in to access module 4.1.2 Quiz4.1.3 Experimental Aspects for Fine Tuning
Log in to access module 4.1.3 Experimental Aspects for Fine Tuning4.1.4 Code Demo – Module 4
Log in to access module 4.1.4 Code Demo – Module 44.1.4 Quiz
Log in to access module 4.1.4 Quiz4.2 Additional Reading
Log in to access module 4.2 Additional ReadingForum – Module 4
Log in to access module Forum – Module 4
6.
Module 5: Wrap Up
Intro
Log in to access module Intro5.1 Review of the Course
Log in to access module 5.1 Review of the Course5.2 State of the Art Tuning and Outlook
Log in to access module 5.2 State of the Art Tuning and Outlook5.3 Additional Reading
Log in to access module 5.3 Additional ReadingForum – Module 5
Log in to access module Forum – Module 5Final Exam
Log in to final quiz https://theschoolofquantum.ont.stuurlui.dev/module/final-exam-2/Discussions
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Quantum Hardware and its Applications with Quantum Inspire
Intermediate
24-30 H
Quantum Applications