Implementation of Quantum Annealing Learning Search (QALS) for solving optimization problems

The project is focused on the implementation and test of the Quantum Annealing Learning Search (QALS) algorithm on the D-Wave Quantum Annealer to solve general QUBO problems, in order to empirically validate a new hybrid quantum-classical learning search. The main result achieved by the project relates to an implementation of QALS algorithm showing that, via the hybrid learning search, the quantum annealer learns the representation of an optimization problem that cannot be directly represented into the hardware architecture due to the sparsity of the qubit graph. Another remarkable result is the first implementation of an already-known algorithm for reconstructing Bayesian networks and of a divide-et-impera approach for the same task.

References: arXiv:2212.11132, arXiv:2204.03526