  Quantum Computing has been evolving in the last years. Although nowadays
quantum algorithms performance has shown superior to their classical
counterparts, quantum decoherence and additional auxiliary qubits needed for
error tolerance routines have been huge barriers for quantum algorithms
efficient use. These restrictions lead us to search for ways to minimize
algorithms costs, i.e the number of quantum logical gates and the depth of the
circuit. For this, quantum circuit synthesis and quantum circuit optimization
techniques are explored. We studied the viability of using Projective
Simulation, a reinforcement learning technique, to tackle the problem of
quantum circuit synthesis for noise quantum computers with limited number of
qubits. The agent had the task of creating quantum circuits up to 5 qubits to
generate GHZ states in the IBM Tenerife (IBM QX4) quantum processor. Our
simulations demonstrated that the agent had a good performance but its capacity
for learning new circuits decreased as the number of qubits increased.
