ESQUIMAL proposes a framework to put together Machine Learning and Physics (particularly, Quantum Computing) in order to:
1.- Enhance the performance of Machine Learning algorithms by means of physics-inspired techniques and quantum mechanics concepts.
2.- Provide shortcuts and alternatives to some major challenges of quantum computing, e.g., adiabaticity, design of quantum circuits, minimization of quantum measurements, etc.
3.- Include physical information in Machine Learning models for a more natural and accurate solution of problems in Physics, e.g., partial differential equations of Relativistic Hydrodynamics.

This project is funded by Generalitat Valenciana – Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital – Grupos de Investigación Consolidados AICO 2022

Status: Ongoing

Start date: 01/01/2022

End date: 12/31/2024