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IQUIST seminar: Recurrent Quantum Neural Networks

Event Type
Seminar/Symposium
Sponsor
IQUIST
Date
Mar 30, 2021   11:00 am  
Speaker
Johannes Bausch, Cambridge University, UK
Contact
Julie Moore
E-Mail
jmoor@illinois.edu
Views
245
Originating Calendar
IQUIST Seminar Series

Recurrent neural networks are the foundation of many sequence-to-sequence models in machine learning, such as machine translation and speech synthesis. In this work we construct a quantum recurrent neural network (QRNN) with demonstrable performance on non-trivial tasks such as sequence learning and integer digit classification. The QRNN cell is built from parametrized quantum neurons, which, in conjunction with amplitude amplification, create a nonlinear activation of polynomials of its inputs and cell state, and allow the extraction of a probability distribution over predicted classes at each step. To study the model's performance, we provide an implementation in pytorch, which allows the relatively efficient optimization of parametrized quantum circuits with thousands of parameters. We establish a QRNN training setup by benchmarking optimization hyperparameters, and analyse suitable network topologies for simple memorisation and sequence prediction tasks from Elman's seminal paper (1990) on temporal structure learning. We then proceed to evaluate the QRNN on MNIST classification, both by feeding the QRNN each image pixel-by-pixel; and by utilising modern data augmentation as preprocessing step. Finally, we analyse to what extent the unitary nature of the network counteracts the vanishing gradient problem that plagues many existing quantum classifiers and classical RNNs.

http://www.damtp.cam.ac.uk/person/jkrb2

To watch online go to the IQUIST youtube channel: https://www.youtube.com/channel/UCCzAySwQXF8J4kRolUzg2ww

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