An efficient algorithm for optimizing adaptive quantum metrology processes
We introduce an efficient self learning swarm intelligence algorithm for devising feedback based quantum metrological procedures to replace what is otherwise an difficult and inefficient problem. Our algorithm can be trained with simulated or real world trials and accommodates experimental imperfections, losses, and decoherence.
2011 International Quantum Electronics Conference (IQEC) and Conference on Lasers and Electro Optics (CLEO) Pacific Rim incorporating the Australasian Conference on Optics, Lasers and Spectroscopy and the Australian Conference on Optical Fibre Technology