  The study of Variational Quantum Eigensolvers (VQEs) has been in the
spotlight in recent times as they may lead to real-world applications of
near-term quantum devices. However, their performance depends on the structure
of the used variational ansatz, which requires balancing the depth and
expressivity of the corresponding circuit. In recent years, various methods for
VQE structure optimization have been introduced but the capacities of machine
learning to aid with this problem has not yet been fully investigated. In this
work, we propose a reinforcement learning algorithm that autonomously explores
the space of possible ans{\"a}tze, identifying economic circuits which still
yield accurate ground energy estimates. The algorithm is intrinsically
motivated, and it incrementally improves the accuracy of the result while
minimizing the circuit depth. We showcase the performance of our algorithm on
the problem of estimating the ground-state energy of lithium hydride (LiH). In
this well-known benchmark problem, we achieve chemical accuracy, as well as
state-of-the-art results in terms of circuit depth.
