  This paper presents the details and testing of two implementations (in C++
and Python) of the hybrid quantum-classical algorithm Quantum Annealing
Learning Search (QALS) on a D-Wave quantum annealer. QALS was proposed in 2019
as a novel technique to solve general QUBO problems that cannot be directly
represented into the hardware architecture of a D-Wave machine. Repeated calls
to the quantum machine within a classical iterative structure and a related
convergence proof originate a learning mechanism to find an encoding of a given
problem into the quantum architecture. The present work considers the Number
Partitioning Problem (NPP) and the Travelling Salesman Problem (TSP) for the
testing of QALS. The results turn out to be quite unexpected, with QALS not
being able to perform as well as the other considered methods, especially in
NPP, where classical methods outperform quantum annealing in general.
Nevertheless, looking at the TSP tests, QALS has fulfilled its primary goal,
i.e., processing QUBO problems not directly mappable to the QPU topology.
