Data-science seminar: Quantum Machine Learning

• Maria Schuld (UKZN)

Room: 222/R-001

Welcome and recap

• Maria Schuld

Room: 222/R-001

Part I: Classical machine learning with automatic differentiation

• Maria Schuld

Room: 222/R-001 Learning objectives: * Be able to explain the concept of automatic differentiation * Be able to train a simple linear model using automatic differentiation

Part II: Differentiable quantum computing

• Maria Schuld

Room: 222/R-001 Learning objectives: * Be able to implement a variational quantum circuit in PennyLane * Compute the gradient of a variational quantum circuit * Train a variational quantum circuit like a machine learning model

Break (there will be an optional task for the break)

• Maria Schuld

Room: 222/R-001

Part III: Quantum gradients on remote devices

• Maria Schuld

Room: 222/R-001 Learning objectives: * Be able to name and explain three different ways to compute quantum gradients * Understand why parameter-shift rules are needed for hardware * Be able to compute a quantum gradient on a remote backend

Final words

• Maria Schuld

Room: 222/R-001