Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical algorithms for use in quantum information
Journal of Computational Physics
Simulation of entanglement generation and variation in quantum computation
Journal of Computational Physics
Numerical analysis of entanglement properties of density matrices in C2⊗C2systems
Quantum Information & Computation
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Quantum entanglement is an enigmatic add powerful property that has attracted much attention, since it provides new means of communication, such as quantum cryptography, quantum teleportation and superdense coding, and quantum computation. Therefore, the measure of entanglement of composite systems becomes crucial. For pure bipartite states the von Neumann entropy is a good measure and it is easily calculated; however, to quantify the entanglement of mixed states is a harder task. Some measures have been proposed, among them one based on relative entropy. This measure needs a minimization procedure. In this paper, we present an algorithm to calculate the quantum entanglement measure based on relative entropy, in which the required minimization procedure is done using a genetic algorithm. The main advantage of using a genetic algorithm is it is easier to implement than gradient methods are when the number of parameters is very large.