Quantum neural computation of entanglement is robust to noise and decoherence

Autor:	Behrman, E. C., Nguyen, N. H., Steck, J. E., McCann, M.
Rok vydání:	2015
Předmět:	Quantum Physics
Zdroj:	Quantum Inspired Computational Intelligence: Research and Applications, S. Bhattacharyya, ed. (Morgan Kaufmann, Elsevier, 2016) rks and Learning Systems 25, 1696-1703 (2014)
Druh dokumentu:	Working Paper
Popis:	In previous work, we have proposed an entanglement indicator for a general multiqubit state, which can be "learned" by a quantum system, acting as a neural network. The indicator can be used for a pure or a mixed state, and it need not be "close" to any particular state; moreover, as the size of the system grows, the amount of additional training necessary diminishes. Here, we show that the indicator is stable to noise and decoherence. Comment: to be published in Quantum Inspired Computational Intelligence (Elsevier, 2016)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1510.09173 Zobrazit plný text záznamu View this record from Arxiv