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Abstract: Adaptive feedback schemes are promising for quantum-enhanced measurements yetare complicated to design. Machine learning can autonomously generatealgorithms in a classical setting. Here we adapt machine learning for quantuminformation and use our framework to generate autonomous adaptive feedbackschemes for quantum measurement. In particular our approach replaces guessworkin quantum measurement by a logical, fully-automatic, programmable routine. Weshow that our method yields schemes that outperform the best known adaptivescheme for interferometric phase estimation.

Author: Alexander Hentschel, Barry C. Sanders



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