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