Abstract
The entanglement is an extremely important property of quantum computations. However, detecting the entanglement is a difficult problem – even if models of quantum systems are simulated with use of classical computers. It is caused by the exponential complexity of quantum systems which implies overloading the computational resources. This problem appears especially when the quantum states are expressed as the density matrices. Fortunately, regarding quantum states as patterns and using artificial neural networks to diagnose the presence of entanglement seems to be an interesting idea, because the experiment described in this chapter shows that neural networks are able to detect the entanglement with high probability of correct classification. Additionally, the process of neural network’s learning needs less computational resources than complete simulation of quantum computations.
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Wiśniewska, J., Sawerwain, M. (2015). Detecting Entanglement in Quantum Systems with Artificial Neural Network. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9011. Springer, Cham. https://doi.org/10.1007/978-3-319-15702-3_35
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DOI: https://doi.org/10.1007/978-3-319-15702-3_35
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