This work successfully employs a novel approach in processing patient and drug data to predict the drug response for cancer patients.

The approach uses a deep quantum computing circuit as part of a machine learning architecture to simultaneously consider the cell line and the chemical and predict its effect.

The resultant hybrid quantum architecture predicted the drug response with 15% better effectiveness than its classical counterpart.

This result presents a step towards designing personalized drugs using the abilities of quantum computers.

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 personalised medicine, where data collection is a challenge.