  Cancer is one of the leading causes of death worldwide. It is caused by a
variety of genetic mutations, which makes every instance of the disease unique.
Since chemotherapy can have extremely severe side effects, each patient
requires a personalized treatment plan. Finding the dosages that maximize the
beneficial effects of the drugs and minimize their adverse side effects is
vital. Deep neural networks automate and improve drug selection. However, they
require a lot of data to be trained on. Therefore, there is a need for
machine-learning approaches that require less data. Hybrid quantum neural
networks were shown to provide a potential advantage in problems where training
data availability is limited. We propose a novel hybrid quantum neural network
for drug response prediction, based on a combination of convolutional, graph
convolutional, and deep quantum neural layers of 8 qubits with 363 layers. We
test our model on the reduced Genomics of Drug Sensitivity in Cancer dataset
and show that the hybrid quantum model outperforms its classical analog by 15%
in predicting IC50 drug effectiveness values. The proposed hybrid quantum
machine learning model is a step towards deep quantum data-efficient algorithms
with thousands of quantum gates for solving problems in personalized medicine,
where data collection is a challenge.
