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arXiv · 1104.3844

An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

Alexander Hentschel·Barry C. Sanders
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Abstract

Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.

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Alexander Hentschel, Barry C. Sanders. 2011-04-19. An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes. https://doi.org/10.1103/physrevlett.107.233601

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