  Quantum computing offers the potential for superior computational
capabilities, particularly for data-intensive tasks. However, the current state
of quantum hardware puts heavy restrictions on input size. To address this,
hybrid transfer learning solutions have been developed, merging pre-trained
classical models, capable of handling extensive inputs, with variational
quantum circuits. Yet, it remains unclear how much each component -- classical
and quantum -- contributes to the model's results. We propose a novel hybrid
architecture: instead of utilizing a pre-trained network for compression, we
employ an autoencoder to derive a compressed version of the input data. This
compressed data is then channeled through the encoder part of the autoencoder
to the quantum component. We assess our model's classification capabilities
against two state-of-the-art hybrid transfer learning architectures, two purely
classical architectures and one quantum architecture. Their accuracy is
compared across four datasets: Banknote Authentication, Breast Cancer
Wisconsin, MNIST digits, and AudioMNIST. Our research suggests that classical
components significantly influence classification in hybrid transfer learning,
a contribution often mistakenly ascribed to the quantum element. The
performance of our model aligns with that of a variational quantum circuit
using amplitude embedding, positioning it as a feasible alternative.
