  In current noisy intermediate-scale quantum devices, hybrid quantum-classical
neural networks (HQNNs) represent a promising solution that combines the
strengths of classical machine learning with quantum computing capabilities.
Compared to classical deep neural networks (DNNs), HQNNs present an additional
set of hyperparameters, which are specific to quantum circuits. These
quantum-specific hyperparameters, such as quantum circuit depth, number of
qubits, type of entanglement, number of shots, and measurement observables, can
significantly impact the behavior of the HQNNs and their capabilities to learn
the given task. In this paper, we investigate the impact of these variations on
different HQNN models for image classification tasks, implemented on the
PennyLane framework. We aim to uncover intuitive and counter-intuitive learning
patterns of HQNN models within granular levels of controlled quantum
perturbations, to form a sound basis for their correlation to accuracy and
training time. The outcome of our study opens new avenues for designing
efficient HQNN algorithms and builds a foundational base for comprehending and
identifying tunable hyperparameters of HQNN models that can lead to useful
design implementation and usage.
