  Optimization problems is one of the most challenging applications of quantum
computers, as well as one of the most relevants. As a consequence, it has
attracted huge efforts to obtain a speedup over classical algorithms using
quantum resources. Up to now, many problems of different nature have been
addressed through the perspective of this revolutionary computation paradigm,
but there are still many open questions. In this work, a hybrid
classical-quantum approach is presented for dealing with the one-dimensional
Bin Packing Problem (1dBPP). The algorithm comprises two modules, each one
designed for being executed in different computational ecosystems. First, a
quantum subroutine seeks a set of feasible bin configurations of the problem at
hand. Secondly, a classical computation subroutine builds complete solutions to
the problem from the subsets given by the quantum subroutine. Being a hybrid
solver, we have called our method H-BPP. To test our algorithm, we have built
18 different 1dBPP instances as a benchmarking set, in which we analyse the
fitness, the number of solutions and the performance of the QC subroutine.
Based on these figures of merit we verify that H-BPP is a valid technique to
address the 1dBPP.
