Machine Learning for Semiconductor Quantum Devices

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

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.

What You'll Learn

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