Corspedia | Machine Learning for Semiconductor Quantum Devices

Overview

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

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

Syllabus

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

Taught by

Eliška Greplová

Machine Learning for Semiconductor Quantum Devices

Brought by: edX

Overview

Syllabus

Taught by

Machine Learning for Semiconductor Quantum Devices

Brought by: edX