Harnessing Quantum Support Vector Machines for Cross-Domain Classification of Quantum States

TL;DR

This paper presents a quantum support vector machine that effectively classifies quantum states into entangled or separable categories across different domains, demonstrating advantages over classical methods.

Contribution

The study introduces a quantum machine learning approach for cross-domain quantum state classification, addressing entanglement and discord detection with improved efficiency.

Findings

Successfully classifies two-qubit states into entangled and separable categories.

Accurately identifies Bell diagonal states with zero and non-zero discord.

Shows robustness under local unitary transformations.

Abstract

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.