  Several applications of quantum machine learning (QML) rely on a quantum
measurement followed by training algorithms using the measurement outcomes.
However, recently developed QML models, such as variational quantum circuits
(VQCs), can be implemented directly on the state of the quantum system (quantum
data). Here, we propose to use a qubit as a probe to estimate the degree of
non-Markovianity of the environment. Using VQCs, we find an optimal sequence of
qubit-environment interactions that yield accurate estimations of the degree of
non-Markovianity for the amplitude damping, phase damping, and the combination
of both models. We introduce a problem-based ansatz that optimizes upon the
probe qubit and the interaction time with the environment. This work
contributes to practical quantum applications of VQCs and delivers a feasible
experimental procedure to estimate the degree of non-Markovianity.
