Quantum Machine Learning

Quantum Machine Learning (QML) is a topic that borrows and puts together concepts from the fields of Machine Learning (ML) and Quantum Computing (QC), with the goal of improving ML algorithms, quantum experiments, or both. Two main approaches can be considered:
a) The use of quantum resources to improve ML in terms of speed-up and/or performance, including the implementation of ML algorithms in quantum computers.
b) Application of ML to quantum experimentation, e.g., Reinforcement Learning for quantum control, Active Learning for reducing the number of quantum measures, etc.

On top of its suitability to Physics-related problems, the use of QML is of singular interest in a number of fields, such as finance or drug design, that can be benefited from fast calculations. In this framework, QML can help QC to carry out a useful quantum advantage, i.e., to get solutions that would be unfeasible with classical computers and/or classical algorithms.

Scientific and transfer collaborations in this field are welcome, including consultancy tasks to analyze and implement QML-based solutions.