Machine Learning for Semiconductor Quantum Devices

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. This course is authored by experts from the QuTech research center at Delft University of Technology. In the center, scientists and engineers work together to drive research and development in quantum technology. QuTech Academy's aim is to inspire, share and disseminate knowledge about the latest developments in quantum technology. 3b

• 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

• Programming in Python
• Basic familiarity with quantum dots equivalent to the following videos: Quantum Computers How to build a qubit Spin qubits Operations on spin qubits Electron spin qubits Capturing a single electron Quantum dot qubits Quantum control and readoutRecommended prerequisites:
• Introductory knowledge of neural networks (we will provide reading material and review this concept at the beginning of the course, but some previous knowledge will better facilitate your learning). Recommended pre-read (Module 1)
• Basic familiarity with PyTorch (we will take time to explain the code in detail, but looking at the PyTorch package before the course starts will be very helpful). Recommended pre-read