Competitive Learning Algorithm of QuantumStates

The entanglement classification of quantum states is nearly related to the positivehomogeneous function that is invariant under stochastic local operations and classicalcommunication (SLOCC) transformations. In this paper, first we give a general schemeto measure the N-tangle as an entanglement measure of an arbitrary odd-N qubit purestate. Then we develop a classification algorithm that is based on the integration betweencompetitive learning and the computational power of quantum computing. Using thisalgorithm the entanglement measure we classify an input into one of two binary classeseven if the input pattern is incomplete.The entanglement quantification and classification of quantum states of a large number ofparticles becomes quickly complex. This major challenge was raised within the quantum information science regarding the physical phenomenon of entanglement as a key resource. Duringthe past years, the polynomial invariants such as concurrence for two-qubit states andthree-tangle for pure three-qubit states was defined