Current students

Machine Learning and optimization algorithms are largely applied to improve the efficiency of energy production and distribution systems. However, they may show performance problems with the scaling of the systems and the consequent increasing of the computational cost.
This is the reason why the research is turning to Quantum Computing, which represents a potential solution to speed up classical algorithms.
However, there are some obstacles to the applicability of quantum computers, including their limited number of qubits and the noise of the sorrounding environment, which leads both to errors in the measurements and to a progressive decoherence of the qubits.
Variational Quantum Algorithms (VQAs), which make use of a parametrized circuit to prepare the quantum state and a classical optimizer to tune the parameters, have shown some potential in addressing these hurdles.
This thesis shows an application of VQAs to environmental problems and proposes heuristics to improve their performances and tackle those issues.