  The rapid advancement of quantum computing (QC) and machine learning (ML) has
given rise to the burgeoning field of quantum machine learning (QML), aiming to
capitalize on the strengths of quantum computing to propel ML forward. Despite
its promise, crafting effective QML models necessitates profound expertise to
strike a delicate balance between model intricacy and feasibility on Noisy
Intermediate-Scale Quantum (NISQ) devices. While complex models offer robust
representation capabilities, their extensive circuit depth may impede seamless
execution on extant noisy quantum platforms. In this paper, we address this
quandary of QML model design by employing deep reinforcement learning to
explore proficient QML model architectures tailored for designated supervised
learning tasks. Specifically, our methodology involves training an RL agent to
devise policies that facilitate the discovery of QML models without
predetermined ansatz. Furthermore, we integrate an adaptive mechanism to
dynamically adjust the learning objectives, fostering continuous improvement in
the agent's learning process. Through extensive numerical simulations, we
illustrate the efficacy of our approach within the realm of classification
tasks. Our proposed method successfully identifies VQC architectures capable of
achieving high classification accuracy while minimizing gate depth. This
pioneering approach not only advances the study of AI-driven quantum circuit
design but also holds significant promise for enhancing performance in the NISQ
era.
