  Large scale optimisation of quantum circuits is a computationally challenging
problem. Reinforcement Learning (RL) is a recent approach for learning
strategies to optimise quantum circuits by increasing the reward of an
optimisation agent. The reward is a function of the quantum circuit costs, such
as gate and qubit counts, or circuit depth. Our goal is to improve the agent's
optimization strategy, by including hints about how quantum circuits are
optimized manually: there are situations when the cost of a circuit should be
allowed to temporary explode, before applying optimisations which significantly
reduce the circuit's cost. We bring numerical evidence, using
Bernstein-Vazirani circuits, to support the advantage of this strategy. Our
results are preliminary, and show that allowing cost explosions offers
significant advantages for RL training, such as reaching optimum circuits. Cost
explosion strategies have the potential to be an essential tool for RL of
large-scale quantum circuit optimisation.
