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.ipynb_checkpoints (0, 2021-02-09)
.ipynb_checkpoints\1-classical-ml-with-automatic-differentiation-checkpoint.ipynb (99206, 2021-02-09)
.ipynb_checkpoints\2-differentiable-quantum-computing-checkpoint.ipynb (18298, 2021-02-09)
.ipynb_checkpoints\3-quantum-gradients-checkpoint.ipynb (29578, 2021-02-09)
1-classical-ml-with-automatic-differentiation.ipynb (99206, 2021-02-09)
2-differentiable-quantum-computing.ipynb (24328, 2021-02-09)
3-quantum-gradients.ipynb (29578, 2021-02-09)
LICENSE (11357, 2021-02-09)
figures (0, 2021-02-09)
figures\1.png (192969, 2021-02-09)
figures\2.png (249426, 2021-02-09)
figures\3.png (347313, 2021-02-09)
figures\4.png (49176, 2021-02-09)
figures\5.png (43397, 2021-02-09)
figures\6.png (45252, 2021-02-09)
figures\7.png (59795, 2021-02-09)
figures\data-model-cost.png (147926, 2021-02-09)
figures\full-model.png (69894, 2021-02-09)
parameter_shift_slides.pdf (2194742, 2021-02-09)
seminar_slides.pdf (3887201, 2021-02-09)

# pennylane-demo-cern This repository contains material for the [PennyLane tutorial at CERN](https://indico.cern.ch/event/893116/) on 3/4 February 2021. Schedule -------- **9:30 - 9:45** Welcome and recap of seminar [(open on github)](https://github.com/XanaduAI/pennylane-demo-cern/blob/main/seminar_slides.pdf) [(open with Google Drive)](https://drive.google.com/file/d/1NxR4iEjHfn-7i30FIMqlp82gD2nmh8j3/view?usp=sharing) **9:45 - 10:30** Part I: Classical machine learning with automatic differentiation *Notebook 1-classical-ml-with-automatic-differentiation* [(open on github)](https://github.com/XanaduAI/pennylane-demo-cern/blob/main/1-classical-ml-with-automatic-differentiation.ipynb) [(open with colab)](https://colab.research.google.com/github/XanaduAI/pennylane-demo-cern/blob/main/1-classical-ml-with-automatic-differentiation.ipynb) Learning objectives: * be able to explain the concept of automatic differentiation, * 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)](https://github.com/XanaduAI/pennylane-demo-cern/blob/main/2-differentiable-quantum-computing.ipynb) [(open with colab)](https://colab.research.google.com/github/XanaduAI/pennylane-demo-cern/blob/main/2-differentiable-quantum-computing.ipynb) 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. **11:15 - 11:45** Break **11:45 - 12:25** Part III: Quantum gradients on remote devices *Slides* [(open on github)](https://github.com/XanaduAI/pennylane-demo-cern/blob/main/parameter_shift_slides.pdf) [(open with Google Drive)](https://docs.google.com/presentation/d/1bwbAVnHQaj8Yl4t-ocpf2wfutzDpG2wmWApzyguNenc/edit?usp=sharing) *Notebook 3-quantum-gradients* [(open on github)](https://github.com/XanaduAI/pennylane-demo-cern/blob/main/3-quantum-gradients.ipynb) [(open with colab)](https://colab.research.google.com/github/XanaduAI/pennylane-demo-cern/blob/main/3-quantum-gradients.ipynb) Learning objectives: * be able to explain two 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. **12:25-12:30** Final words

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