title: Efficient algorithm for optimizing adaptive quantum metrology processes.
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.
authors.affiliation: Institute for Quantum Information Science, University of Calgary, Calgary, Alberta, Canada T2N 1N4. A.Hentschel@ucalgary.ca
authors.forename: Alexander
authors.initials: A
authors.lastname: Hentschel
authors.forename: Barry C
authors.initials: BC
authors.lastname: Sanders