Quantum circuit noise simulation with reinforcement learning

Quantum computing in the NISQ era requires powerful tools to reduce the gap between simulations and quantum hardware execution. In this work, we present a machine learning approach for reproducing the noise of a specific quantum device during simulations. The proposed algorithm is meant to be more flexible, in reproducing different noise conditions, than standard techniques like randomized benchmarking or heuristic noise models. This model has been tested both with simulation and on real superconducting qubits.