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.

翻译：本研究采用量子机器学习进行跨域分类，以量子优势重新审视纠缠与可分离性范式。我们利用量子态的固有结构及其与特定量子态类别的关系，直观地对来自与训练态不同领域的测试态进行分类，称为\textit{跨域分类}。通过我们的量子机器学习算法，我们实现了将双量子比特混合态高效分类为纠缠类与可分离类。为分析关联的量子特性，我们的模型能够将Bell对角态充分分类为零discord态与非零discord态。此外，我们还通过随机局部酉变换扩展了分析，以评估模型的鲁棒性。与经典支持向量机和神经网络相比，我们的结果证明了量子支持向量机在多维希尔伯特空间中分类量子态的潜力。