An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

Details An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

2011-04-19

arxiv.org/abs/1104.3844v1

Physics

Quantum Physics

Scientific paper

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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