An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes
Quantum Physics
2015-03-19 v1
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.
Cite
@article{arxiv.1104.3844,
title = {An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes},
author = {Alexander Hentschel and Barry C. Sanders},
journal= {arXiv preprint arXiv:1104.3844},
year = {2015}
}