  Variational Quantum Algorithms are one of the most promising candidates to
yield the first industrially relevant quantum advantage. Being capable of
arbitrary function approximation, they are often referred to as Quantum Neural
Networks (QNNs) when being used in analog settings as classical Artificial
Neural Networks (ANNs). Similar to the early stages of classical machine
learning, known schemes for efficient architectures of these networks are
scarce. Exploring beyond existing design patterns, we propose a reduced-width
circuit ansatz design, which is motivated by recent results gained in the
analysis of dropout regularization in QNNs. More precisely, this exploits the
insight, that the gates of overparameterized QNNs can be pruned substantially
until their expressibility decreases. The results of our case study show, that
the proposed design pattern can significantly reduce training time while
maintaining the same result quality as the standard "full-width" design in the
presence of noise.
