  Designing optimal control pulses that drive a noisy qubit to a target state
is a challenging and crucial task for quantum engineering. In a situation where
the properties of the quantum noise affecting the system are dynamic, a
periodic characterization procedure is essential to ensure the models are
updated. As a result, the operation of the qubit is disrupted frequently. In
this paper, we propose a protocol that addresses this challenge by making use
of a spectator qubit to monitor the noise in real-time. We develop a
machine-learning-based quantum feature engineering approach for designing the
protocol. The complexity of the protocol is front-loaded in a characterization
phase, which allow real-time execution during the quantum computations. We
present the results of numerical simulations that showcase the favorable
performance of the protocol.
