Chapter 1 -- Quantum neural computation of entanglement is robust to noise and decoherence
Behrman, Elizabeth C. Nguyen, Nam H. Steck, James E. McCann, M.
Behrman, Elizabeth C.
Nguyen, Nam H.
Steck, James E.
McCann, M.
Citations
Altmetric:
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2017
Type
Book chapter
Genre
Keywords
Quantum algorithm,Entanglement,Dynamic learning,Noise,Decoherence
Subjects (LCSH)
Citation
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
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.
Table of Contents
Description
Click on the DOI link to access the article (may not be free).
Publisher
Elsevier
Journal
Book Title
Series
Quantum Inspired Computational Intelligence Research and Applications;2017