Classical computational models often fail to capture the complexity of oncological data especially in the case of rare cancer for which there are no standard treatments due to fewer and fragmented data currently available. Machine learning and quantum computing are two technologies with the potential to revolutionise how computation is performed to address previously untenable problems. This project aims to exploit the potential of quantum machine learning algorithms to link new undetected target patterns and results from publicly available cell-line-based pharmacogenomic screens for drug repurposing in rare tumors. In addition our findings will be integrated in a web interface to help clinicians choose the more suitable therapy for diseases that still lack treatment options like most rare cancers.