ArXiv Preprint
Quantum machine learning is receiving significant attention currently, but
its usefulness in comparison to classical machine learning techniques for
practical applications remains unclear. However, there are indications that
certain quantum machine learning algorithms might result in improved training
capabilities with respect to their classical counterparts -- which might be
particularly beneficial in situations with little training data available. Such
situations naturally arise in medical classification tasks. Within this paper,
different hybrid quantum-classical convolutional neural networks (QCCNN) with
varying quantum circuit designs and encoding techniques are proposed. They are
applied to two- and three-dimensional medical imaging data, e.g. featuring
different, potentially malign, lesions in computed tomography scans. The
performance of these QCCNNs is already similar to the one of their classical
counterparts -- therefore encouraging further studies towards the direction of
applying these algorithms within medical imaging tasks.
Andrea Matic, Maureen Monnet, Jeanette Miriam Lorenz, Balthasar Schachtner, Thomas Messerer
2022-04-26
