This repository contains material for the PennyLane tutorial at CERN on 3/4 February 2021.
9:30 - 9:45 Welcome and recap of seminar (open on github) (open with Google Drive)
9:45 - 10:30 Part I: Classical machine learning with automatic differentiation
Notebook 1-classical-ml-with-automatic-differentiation (open on github) (open with colab)
Learning objectives:
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be able to explain the concept of automatic differentiation,
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be able to train a simple linear model using automatic differentiation.
10:30 - 11:15 Part II: Differentiable quantum computing
Notebook 2-differentiable-quantum-computations (open on github) (open with colab)
Learning objectives:
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be able to implement a variational quantum circuit in PennyLane,
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compute the gradient of a variational quantum circuit,
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train a variational quantum circuit like a machine learning model.
11:15 - 11:45 Break
11:45 - 12:25 Part III: Quantum gradients on remote devices
Slides (open on github) (open with Google Drive)
Notebook 3-quantum-gradients (open on github) (open with colab)
Learning objectives:
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be able to explain two different ways to compute quantum gradients,
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understand why parameter-shift rules are needed for hardware,
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be able to compute a quantum gradient on a remote backend.
12:25-12:30 Final words