  Quantum Computing is considered as the next frontier in computing, and it is
attracting a lot of attention from the current scientific community. This kind
of computation provides to researchers with a revolutionary paradigm for
addressing complex optimization problems, offering a significant speed
advantage and an efficient search ability. Anyway, Quantum Computing is still
in an incipient stage of development. For this reason, present architectures
show certain limitations, which have motivated the carrying out of this paper.
In this paper, we introduce a novel solving scheme coined as hybrid Quantum
Computing - Tabu Search Algorithm. Main pillars of operation of the proposed
method are a greater control over the access to quantum resources, and a
considerable reduction of non-profitable accesses. To assess the quality of our
method, we have used 7 different Traveling Salesman Problem instances as
benchmarking set. The obtained outcomes support the preliminary conclusion that
our algorithm is an approach which offers promising results for solving
partitioning problems while it drastically reduces the access to quantum
computing resources. We also contribute to the field of Transfer Optimization
by developing an evolutionary multiform multitasking algorithm as
initialization method.
