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Quantum Computing has been evolving in the last years. Although nowadaysquantum algorithms performance has shown superior to their classicalcounterparts, quantum decoherence and additional auxiliary qubits needed forerror tolerance routines have been huge barriers for quantum algorithmsefficient use. These restrictions lead us to search for ways to minimizealgorithms costs, i.e the number of quantum logical gates and the depth of thecircuit. For this, quantum circuit synthesis and quantum circuit optimizationtechniques are explored. We studied the viability of using ProjectiveSimulation, a reinforcement learning technique, to tackle the problem ofquantum circuit synthesis for noise quantum computers with limited number ofqubits. The agent had the task of creating quantum circuits up to 5 qubits togenerate GHZ states in the IBM Tenerife (IBM QX4) quantum processor. Oursimulations demonstrated that the agent had a good performance but its capacityfor learning new circuits decreased as the number of qubits increased.

Quantum circuit synthesis of Bell and GHZ states using projective simulation in the NISQ era