  In the present study, we use cross-domain classification using quantum
machine learning for quantum advantages to readdress the entanglement versus
separability paradigm. The inherent structure of quantum states and its
relation to a particular class of quantum states are used to intuitively
classify testing states from domains different from training states, called
\textit{cross-domain classification}. Using our quantum machine learning
algorithm, we demonstrate efficient classifications of two-qubit mixed states
into entangled and separable classes. For analyzing the quantumness of
correlations, our model adequately classifies Bell diagonal states as zero and
non-zero discord states. In addition, we also extend our analysis to evaluate
the robustness of our model using random local unitary transformations. Our
results demonstrate the potential of the quantum support vector machine for
classifying quantum states across the multi-dimensional Hilbert space in
comparison to classical support vector machines and neural networks.
