  Applying new computing paradigms like quantum computing to the field of
machine learning has recently gained attention. However, as high-dimensional
real-world applications are not yet feasible to be solved using purely quantum
hardware, hybrid methods using both classical and quantum machine learning
paradigms have been proposed. For instance, transfer learning methods have been
shown to be successfully applicable to hybrid image classification tasks.
Nevertheless, beneficial circuit architectures still need to be explored.
Therefore, tracing the impact of the chosen circuit architecture and
parameterization is crucial for the development of beneficially applicable
hybrid methods. However, current methods include processes where both parts are
trained concurrently, therefore not allowing for a strict separability of
classical and quantum impact. Thus, those architectures might produce models
that yield a superior prediction accuracy whilst employing the least possible
quantum impact. To tackle this issue, we propose Sequential Quantum Enhanced
Training (SEQUENT) an improved architecture and training process for the
traceable application of quantum computing methods to hybrid machine learning.
Furthermore, we provide formal evidence for the disadvantage of current methods
and preliminary experimental results as a proof-of-concept for the
applicability of SEQUENT.
