Continuous Variable Quantum Classifiers: MNIST

[sophchoe]

General information

Name
Sophie Choe

Affiliation (optional)
Portland State University, Electrical and Computer Engineering

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Demo information

Title
Continuous Variable Quantum Classifiers: MNIST.

Abstract
MNIST dataset classifiers using different number of qumodes: classical and CV quantum hybrid networks.

Relevant links
Demo link
Pre-print paper link
Continuous variable quantum neural networks, Killoran et al., 2019

[CatalinaAlbornoz]

Thank you for submitting this Demo @sophchoe! We will be reviewing it and letting you know when it's up on the website.

[CatalinaAlbornoz]

Hi @sophchoe! I'm very sorry for the delay in my response.

We have complemented the abstract information with the info that you had in the repo. This is how it's looking. Do you agree with the changes?

We built 8 MNIST dataset classifiers using 2-8 qumodes. This family of MNIST classifiers are classical-quantum hybrid circuits using Keras and PennyLane. The quantum circuit is composed of a data encoding circuit and a quantum neural network circuit as proposed in the paper "Continuous variable quantum neural networks" by Killoran et al. The PennyLane-TensorFlow interface converts the quantum circuit into a Keras layer, and the whole network is treated as a Keras network, to which Keras' built in loss function and optimizer can be applied for parameter updates. Categorical cross-entropy is used as the loss function and Stochastic Gradient Descent is used for the optimizer.
Author affiliation: Portland State University, Electrical and Computer Engineering.

Also, the 4_qumode_classifier notebook does not run because the init_layer and layer functions are called in the quantum device, but they're not defined previously. Therefore, the classifier does not run (only in the 4 qumode case). If you can fix that detail we can post the demo on the website tomorrow!

[CatalinaAlbornoz]

Hi @sophchoe, please let us know when you have updated your demo and if you agree with the new description so that we can feature your Demo on the PennyLane website.