Applications of Quantum Machine Learning

Quantum machine learning (QML) algorithms are a promising approach to certain classes of applications in HEP phenomenology and theory, for example using variational quantum circuits and novel model architectures, which could benefit from the quantum hardware representation. The main motivation behind this planned activity is to identify the potential benefits of QML in HEP in terms of performance, precision, accuracy, and power consumption when compared to the current classical state of the art counterparts.
We will target applications on practical primitives for near-term quantum devices as well as advanced procedures for future, fully-fledged universal quantum computers. Research activities will include the development of hybrid classical-quantum models based on variational quantum circuit optimization and data re-uploading techniques, and the identification of new hardware designs which may accelerate QML performance. An important goal of this activity is the joint development of open-source libraries of quantum machine learning models designed for the hep-ph and hep-th applications based on open collaboration across the community. This activity is the natural bridge between the Theory and Simulation area and the Quantum Computing area of the CERN QTI.