Quantum Machine Learning and Deep Learning in Oncology

Quantum Machine Learning (QML) is an emerging field that merges quantum computing with artificial intelligence to tackle complex computational problems. By leveraging quantum principles, QML algorithms offer the potential to process high-dimensional data more efficiently than classical methods. This capability is particularly valuable in healthcare, where QML is being explored for applications in medical imaging, drug discovery, and personalized medicine.

In oncology, QML has shown promising results in tumor detection and classification. Hybrid models that integrate classical deep learning techniques, such as convolutional neural networks, with quantum circuits are being developed to enhance the analysis of medical images. These approaches aim to capitalize on quantum advantages while addressing the limitations of current quantum hardware, such as noise and restricted qubit availability.

As these technologies mature, QML is expected to play a pivotal role in precision medicine by enabling faster, more interpretable, and computationally efficient solutions.

This proposal focuses on applying QML and deep learning techniques to five major cancer types: brain, breast, lung, cervical, and kidney cancers. The objective is to develop robust models that improve diagnostic accuracy, support risk prediction, and optimize treatment strategies, ultimately contributing to more effective and personalized cancer care.

Faculty Supervisor:

Moulay Akhloufi

Student:

Partner:

Federal University of Parana

Discipline:

Computer science

Sector:

Education

University:

Université de Moncton

Program: