Scalable Variational Quantum Circuits for Autoencoder-based Drug
Discovery
- URL: http://arxiv.org/abs/2112.12563v1
- Date: Mon, 15 Nov 2021 00:26:19 GMT
- Title: Scalable Variational Quantum Circuits for Autoencoder-based Drug
Discovery
- Authors: Junde Li and Swaroop Ghosh
- Abstract summary: Variational autoencoder is one of the computer-aided design methods which explores the chemical space based on existing molecular dataset.
We present a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a corresponding vanilla variant (SQ-AE) for better reconstruction.
- Score: 8.871042314510788
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The de novo design of drug molecules is recognized as a time-consuming and
costly process, and computational approaches have been applied in each stage of
the drug discovery pipeline. Variational autoencoder is one of the
computer-aided design methods which explores the chemical space based on
existing molecular dataset. Quantum machine learning has emerged as an atypical
learning method that may speed up some classical learning tasks because of its
strong expressive power. However, near-term quantum computers suffer from
limited number of qubits which hinders the representation learning in high
dimensional spaces. We present a scalable quantum generative autoencoder
(SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a
corresponding vanilla variant (SQ-AE) for better reconstruction. The
architectural strategies in hybrid quantum classical networks such as,
adjustable quantum layer depth, heterogeneous learning rates, and patched
quantum circuits are proposed to learn high dimensional dataset such as,
ligand-targeted drugs. Extensive experimental results are reported for
different dimensions including 8x8 and 32x32 after choosing suitable
architectural strategies. The performance of quantum generative autoencoder is
compared with the corresponding classical counterpart throughout all
experiments. The results show that quantum computing advantages can be achieved
for normalized low-dimension molecules, and that high-dimension molecules
generated from quantum generative autoencoders have better drug properties
within the same learning period.
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