Quantum Summer School: QML and QAOA with Qiskit

Explore quantum computing with Qiskit through guided beginner activities in quantum machine learning and optimisation.
About this Event

Join us at Deakin Downtown for a beginner-friendly Quantum Summer School exploring quantum computing through Qiskit and two guided case studies.

The morning introduces computing, data and probability, followed by the quantum concepts needed for the practical sessions. You will then explore Qiskit and quantum machine learning (QML), leading into a case study on simple classification.

The afternoon focuses on optimisation. After an introduction to optimisation problems, you will explore the Quantum Approximate Optimisation Algorithm (QAOA), its circuits and its application to a small optimisation problem.

Guided notebook activities will help you connect the concepts with practical examples. Please bring your own laptops. The day concludes by bringing the two case studies together, discussing their limitations and identifying next steps for learning.

The school is designed for beginners. Previous programming experience and quantum physics knowledge may be helpful but is not required.

Agenda

🕑: 09:00 AM - 09:15 AM
Welcome and orientation
🕑: 09:15 AM - 09:40 AM
Computing, data and probability
🕑: 09:40 AM - 10:40 AM
The quantum ideas we need
🕑: 10:40 AM - 11:00 AM
Morning break
🕑: 11:00 AM - 11:25 AM
Qiskit foundations
🕑: 11:25 AM - 11:45 AM
QML foundations
🕑: 11:45 AM - 12:30 PM
Case study one: QML for classification
🕑: 12:30 PM - 01:30 PM
Lunch break
🕑: 01:30 PM - 02:00 PM
Optimisation foundations
🕑: 02:00 PM - 02:45 PM
QAOA foundations and circuits
🕑: 02:45 PM - 03:05 PM
Afternoon break
🕑: 03:05 PM - 03:50 PM
Case study two: QAOA for a small optimisation problem
🕑: 03:50 PM - 04:20 PM
Conclusion and next steps
🕑: 04:20 PM - 04:30 PM
Closing