Chapter 1 -- Quantum neural computation of entanglement is robust to noise and decoherence

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Authors
Behrman, Elizabeth C.
Nguyen, Nam H.
Steck, James E.
McCann, M.
Issue Date
2017
Type
Book chapter
Language
en_US
Keywords
Quantum algorithm , Entanglement , Dynamic learning , Noise , Decoherence
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Abstract

Measurement and witnesses of entanglement remain an important issue in quantum computing. Most witnesses will work for only a very restricted class of states, while measurements commonly require lengthy procedures. Quantum neural entanglement indicators are both more general and easier to implement. The neural network entanglement indicator can be used for a pure or a mixed state, and the system 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.

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Behrman E.C., Steck J.E. Programming quantum annealing computers using machine learning 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, Volumes 2017-January, 2017
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Elsevier
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