Quantum machine learning models and learning algorithms (2018–)

Quantum machine learning is in its early days and the development of learning models enhanced by quantum computation is not a trivial task. The main problem addressed in this project will be the development of new models of machine learning through the use of quantum computation techniques. In this project will be given continuity to previous studies where training algorithms for quantum neural networks are presented or analyzed and other quantum enhanced models of machine learning will be analyzed.

Quantum enhanced cross-validation for near-optimal neural networks architecture selection (2018)

Neural Networks Architecture Evaluation in a Quantum Computer (2017)

Integration of heterogeneous information sources for drug-target interaction prediction (2018–)

Drug-target networks are receiving a lot of attention in late years, given its relevance for pharmaceutical innovation and drug lead discovery. Different in silico approaches have been proposed for the identification of new drug-target interactions, many of which are based on kernel methods. Despite technical advances in the latest years, these methods are not able to cope with large drug-target interaction spaces and to integrate multiple sources of biological information. In this project, we aim to investigate techniques that allow the integration of multiple heterogeneous information sources for the identification of new interactions, and can also work with networks of arbitrary size.