Day 1: Quantum-Classical Hybrid Models

• Topics: Hybrid quantum-classical algorithms, variational circuits.
• Assignment: Design a hybrid model for a simple problem.
• Resources: TensorFlow Quantum documentation.

Day 2: Quantum Machine Learning Applications

• Topics: Quantum machine learning in real-world scenarios, quantum-enhanced AI.
• Assignment: Explore a case study where quantum computing enhances AI.
• Resources: Quantum Machine Learning 10.

Day 3: Quantum AI Challenges

• Topics: Current limitations, noise, decoherence, and error mitigation strategies.
• Assignment: Discuss strategies for error mitigation in quantum AI.
• Resources: Quantum Computing and Quantum Information 32.

Day 4: Quantum AI Projects

• Topics: Project planning, selecting a quantum AI problem to solve.
• Assignment: Draft a project proposal for the capstone project.
• Resources: Quantum Computing and Quantum Information 32.

Day 5: Capstone Project Introduction

• Topics: Overview of the capstone project, expectations, and deliverables.
• Assignment: Begin research for the capstone project.
• Resources: All previous resources, plus project management tools like Trello or Asana.