Optimal Classification of Three-Qubit Entanglement with Cascaded Support Vector Machine

• URL: http://arxiv.org/abs/2602.15545v1
• Date: Tue, 17 Feb 2026 12:49:41 GMT
• Title: Optimal Classification of Three-Qubit Entanglement with Cascaded Support Vector Machine
• Abstract summary: We introduce a systematic framework for three-qubit entanglement classification using a cascaded architecture of Support Vector Machine (SVM) classifiers.<n>We construct three distinct witness models ($mathcalM_B$, $mathcalM_W$, and $mathcalM_GHZ$) that sequentially discriminate between entanglement classes.<n>The proposed Cascaded model achieves an overall classification accuracy of $95%$ on a comprehensive dataset of mixed states.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a systematic framework for three-qubit entanglement classification using a cascaded architecture of Support Vector Machine (SVM) classifiers. Leveraging the well defined three-qubit structure with the four nested entanglement classes (S, B, W, and GHZ), we construct three distinct witness models ($\mathcal{M}_{B}$, $\mathcal{M}_{W}$, and $\mathcal{M}_{GHZ}$) that sequentially discriminate between these classes. The proposed Cascaded model achieves an overall classification accuracy of $95\%$ on a comprehensive dataset of mixed states. The framework's robustness and generalization capabilities are confirmed through rigorous testing against out-of-distribution (OOD) entangled states and various quantum noise channels, where the model maintains high performance. A key contribution of this research is an optimization protocol based on systematic feature importance analysis. This approach yields a tunable framework that significantly reduces the number of required features, while maintaining reliable model accuracy.

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