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arXiv:1810.03674 · Quantum Information Processing

Detection of genuine n-qubit entanglement via the proportionality of two vectors

Dafa Li

Published May 15, 2019• 4 citations
arXivHTMLPDF
DOI
10.1007/s11128-019-2316-4

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In [Science 340, 1205, 7 June (2013)], via polytopes Michael Walter et al. proposed a sufficient condition detecting the genuinely entangled pure states. In this paper, assume that a state with six non-zero coefficients is not a trivially separable state. Then the state is separable if and only if its six basis states consist of the three partially complementary pairs and the corresponding coefficient matrix has proportional rows. The contrapositive of this result reads that the state is genuinely entangled if and only if its six basis states do not consist of the three partially complementary pairs or though the six basis states consist of the three partially complementary pairs, the corresponding coefficient matrix does not have proportional rows. We propose four corresponding coefficient 2 by 3 matrices and show that if the four coefficient matrices don't have proportional rows, then the state is genuinely entangled. It is trivial to know if two rows of a 2 by 3 coefficient matrix are proportional. The difference from the previous articles is that the structure of the basis states is used to detect entanglement in this paper. One can see that Osterloh and Siewert's states of five and six qubits are genuinely entangled because two rows for any one of the four corresponding coefficient 2 by 3 matrices are not proportional. These states were distinguished as the maximal entangled states by the complicated filters before. Keywords: entanglement, separability, entangled states, separable states, qubits.
In recent years, supervised and semisupervised machine learning methods such as neural networks, support vector machines (SVMs), and semisupervised support vector machines (S4VMs) have been widely used in quantum entanglement and quantum steering verification problems. However, few studies have focused on detecting genuine multipartite entanglement based on machine learning. Here, we investigate supervised and semisupervised machine learning for detecting genuine multipartite entanglement of three-qubit states. We randomly generate three-qubit density matrices and train an SVM for the detection of genuine multipartite entangled states. Moreover, we improve the S4VM training method, which optimizes the grouping of prediction samples and then performs iterative predictions. Through numerical simulation, it is confirmed that this method can significantly improve the prediction accuracy.
We propose a criterion for the detection of genuine entanglement of pure multiqubit states. To this aim, we define an operator called the losing one qubit operator, which is different from the reduced density operator. The states obtained from a multiqubit state by applying the losing one qubit operator are referred to as its projected states. We show that all of the projected states of a pure product n-qubit state are pure product states provided that it cannot be written as a product of a single qubit state and a genuinely entangled (n-1)-qubit state. We also show that a pure n-qubit state is genuinely entangled provided that the state has at least two genuinely entangled (n-1)-qubit projected states. By repeating the losing process, we reduce the detection of entanglement of pure n-qubit states to the one of pure two-qubit states. Also we write a LISP program for the reduction process.