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dc.contributor.authorThompson, Nathan L.
dc.contributor.authorNguyen, Nam H.
dc.contributor.authorBehrman, Elizabeth C.
dc.contributor.authorSteck, James E.
dc.identifier.citationCite this article Thompson, N.L., Nguyen, N.H., Behrman, E.C. et al. Experimental pairwise entanglement estimation for an N-qubit system. Quantum Inf Process 19, 394 (2020)en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractDesigning and implementing algorithms for medium- and large-scale quantum computers is not easy. In the previous work, we have suggested, and developed, the idea of using machine learning techniques to train a quantum system such that the desired process is “learned,” thus obviating the algorithm design difficulty. This works quite well for small systems. But the goal is macroscopic physical computation. Here, we implement our learned pairwise entanglement witness on Microsoft’s Q#, one of the commercially available gate model quantum computer simulators; we perform statistical analysis to determine reliability and reproducibility; and we show that after training the system in stages for an incrementing number of qubits (2, 3, 4,..) we can infer the pattern for mesoscopic N from simulation results for three-, four-, five-, six-, and seven-qubit systems. Our results suggest a fruitful pathway for general quantum computer algorithm design and for practical computation on noisy intermediate-scale quantum devices.en_US
dc.description.sponsorshipHenry Elliott (WSU) for the comparative Qiskit [35] calculations and hardware implementation.en_US
dc.relation.ispartofseriesQuantum Information Processing;
dc.subjectQuantum gatesen_US
dc.subjectQuantum machine learningen_US
dc.subjectQuantum simulatoren_US
dc.titleExperimental pairwise entanglement estimation for an N-qubit system: a machine learning approach for programming quantum hardwareen_US
dc.rights.holder© 2020, Springer Science+Business Media, LLCen_US

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