Quantum neural computation of entanglement is robust to noise and decoherence
Quantum Physics
2017-02-07 v2
Abstract
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.
Keywords
Cite
@article{arxiv.1510.09173,
title = {Quantum neural computation of entanglement is robust to noise and decoherence},
author = {E. C. Behrman and N. H. Nguyen and J. E. Steck and M. McCann},
journal= {arXiv preprint arXiv:1510.09173},
year = {2017}
}
Comments
to be published in Quantum Inspired Computational Intelligence (Elsevier, 2016)