  In the current era of quantum computing, robust and efficient tools are
essential to bridge the gap between simulations and quantum hardware execution.
In this work, we introduce a machine learning approach to characterize the
noise impacting a quantum chip and emulate it during simulations. Our algorithm
leverages reinforcement learning, offering increased flexibility in reproducing
various noise models compared to conventional techniques such as randomized
benchmarking or heuristic noise models. The effectiveness of the RL agent has
been validated through simulations and testing on real superconducting qubits.
Additionally, we provide practical use-case examples for the study of renowned
quantum algorithms.
