  Quantum machine learning models use encoding circuits to map data into a
quantum Hilbert space. While it is well known that the architecture of these
circuits significantly influences core properties of the resulting model, they
are often chosen heuristically. In this work, we present a novel approach using
reinforcement learning techniques to generate problem-specific encoding
circuits to improve the performance of quantum machine learning models. By
specifically using a model-based reinforcement learning algorithm, we reduce
the number of necessary circuit evaluations during the search, providing a
sample-efficient framework. In contrast to previous search algorithms, our
method uses a layered circuit structure that significantly reduces the search
space. Additionally, our approach can account for multiple objectives such as
solution quality, hardware restrictions and circuit depth. We benchmark our
tailored circuits against various reference models, including models with
problem-agnostic circuits and classical models. Our results highlight the
effectiveness of problem-specific encoding circuits in enhancing QML model
performance.
