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

Hybrid quantum neural network for drug response prediction