  With recent advancements in quantum computing technology, optimizing quantum
circuits and ensuring reliable quantum state preparation have become
increasingly vital. Traditional methods often demand extensive expertise and
manual calculations, posing challenges as quantum circuits grow in qubit- and
gate-count. Therefore, harnessing machine learning techniques to handle the
growing variety of gate-to-qubit combinations is a promising approach. In this
work, we introduce a comprehensive reinforcement learning environment for
quantum circuit synthesis, where circuits are constructed utilizing gates from
the the Clifford+T gate set to prepare specific target states. Our experiments
focus on exploring the relationship between the depth of synthesized quantum
circuits and the circuit depths used for target initialization, as well as
qubit count. We organize the environment configurations into multiple
evaluation levels and include a range of well-known quantum states for
benchmarking purposes. We also lay baselines for evaluating the environment
using Proximal Policy Optimization. By applying the trained agents to benchmark
tests, we demonstrated their ability to reliably design minimal quantum
circuits for a selection of 2-qubit Bell states.
