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Several applications of quantum machine learning (QML) rely on a quantummeasurement 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 (quantumdata). Here, we propose to use a qubit as a probe to estimate the degree ofnon-Markovianity of the environment. Using VQCs, we find an optimal sequence ofqubit-environment interactions that yield accurate estimations of the degree ofnon-Markovianity for the amplitude damping, phase damping, and the combinationof both models. We introduce a problem-based ansatz that optimizes upon theprobe qubit and the interaction time with the environment. This workcontributes to practical quantum applications of VQCs and delivers a feasibleexperimental procedure to estimate the degree of non-Markovianity.

Estimating the degree of non-Markovianity using variational quantum circuits