Relatore: Elisa Ercolessi, Università di Bologna


In statistics, Bayesian techniques are known to be good candidates for the optimization of functions that are hard to evaluate and, at the same time they are known to show a reduction in the total number of measurements required, as compared to non-adaptive methods.  
We develop such technique for problems whose solution can be encoded in a cost quantum Hamiltonian of which one wants to find the ground state via a parameter-dependent evolution, such as  the Quantum Approximate Optimization Algorithm (QAOA) as applied for example to MaxCut or other combinatorial problems. 
Our central focus is the implementation of such a Bayesian adaptive protocol on a NISQ device,  more specifically on the PASQAL platform.