This multi-disciplinary PhD thesis will be focused on the design, development and application of novel methods for data analysis based on quantum computing, in what is known as quantum machine learning (QML). Quantum computing is known to outperform classical computers in tasks such as unstructured search (Grover’s algorithm) and factorization (Shor’s algorithm), fundamental to applications such as cryptography. QML explores the potential benefits of quantum representations and mechanisms such as superposition and entanglement for massively parallel processing. Current research is limited by the state of quantum technology, which is in its infancy but developing at an impressive pace.

This PhD candidate will carry out both methodological and applied research towards exploring the potential of QML for healthcare. Related work in our group has focused on quantum computing embeddings for medical imaging data and quantum machine learning algorithms for classification of lung cancer. Current on-going research also includes preliminary work on quantum generative models, such as quantum GANs3, quantum VAEs4 and quantum circuit Born machines. We will explore the use of these techniques for health data, as well as devising novel approaches based on quantum adaptations of algorithms such as optimal transport normalizing flows, which are being developed by our group to analyze medical imaging data.

In a related parallel line of work, we will also explore quantum-inspired algorithms, such as those based on tensor networks, for which promising preliminary results have been reported for big data analysis tasks. The applicability of these techniques for multimodal clinical data exploration will be studied.

More details at https://www.upf.edu/web/mdm-dtic/positions