  Variational quantum algorithms (VQA) have emerged as a promising quantum
alternative for solving optimization and machine learning problems using
parameterized quantum circuits (PQCs). The design of these circuits influences
the ability of the algorithm to efficiently explore the solution space and
converge to more optimal solutions. Choosing an appropriate circuit topology,
gate set, and parameterization scheme is determinant to achieve good
performance. In addition, it is not only problem-dependent, but the quantum
hardware used also has a significant impact on the results. Therefore, we
present BPQCO, a Bayesian Optimization-based strategy to search for optimal
PQCs adapted to the problem to be solved and to the characteristics and
limitations of the chosen quantum hardware. To this end, we experimentally
demonstrate the influence of the circuit design on the performance obtained for
two classification problems (a synthetic dataset and the well-known Iris
dataset), focusing on the design of the circuit ansatz. In addition, we study
the degradation of the obtained circuits in the presence of noise when
simulating real quantum computers. To mitigate the effect of noise, two
alternative optimization strategies based on the characteristics of the quantum
system are proposed. The results obtained confirm the relevance of the
presented approach and allow its adoption in further work based on the use of
PQCs.
