Experimental pairwise entanglement estimation for an N-qubit system: a machine learning approach for programming quantum hardware
Thompson, Nathan L. Nguyen, Nam H. Behrman, Elizabeth C. Steck, James E.
Thompson, Nathan L.
Nguyen, Nam H.
Behrman, Elizabeth C.
Steck, James E.
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2020-11-03
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Article
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Entanglement,Quantum gates,Quantum machine learning,Quantum simulator
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Cite 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)
Abstract
Designing 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.
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Springer
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Quantum Information Processing;v.19:no.11:art.no.394
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1570-0755