
"Differential Evolution for Many-Particle Adaptive Quantum Metrology" N.B. Lovett, C. Crosnier, M, Perarnau-Llobet, B.C. Sanders PRL 110, 220501 (2013) http://prl.aps.org/abstract/PRL/v110/i22/e220501 My introductory comments: "The May 31 issue of PRL has a cover image of something I've been interested in for a few years, namely models of collective motion in self-propelled particles (like bird flocks), often called swarming. This paper presents a new general approach to problems like these, linking them with topics seemingly unrelated, like interferometry, genetic fitness testing, and random walks, with huge computing speed gains. I'll try to figure out what they've said and hopefully explain it." The article abstract: "We devise powerful algorithms based on differential evolution for adaptive many-particle quantum metrology. Our new approach delivers adaptive quantum metrology policies for feedback control that are orders-of-magnitude more efficient and surpass the few-dozen-particle limitation arising in methods based on particle-swarm optimization. We apply our method to the binary-decision-tree model for quantum-enhanced phase estimation as well as to a new problem: a decision tree for adaptive estimation of the unknown bias of a quantum coin in a quantum walk and show how this latter case can be realized experimentally."

"Differential Evolution for Many-Particle Adaptive Quantum Metrology"
N.B. Lovett, C. Crosnier, M, Perarnau-Llobet, B.C. Sanders
PRL 110, 220501 (2013)
http://prl.aps.org/abstract/PRL/v110/i22/e220501

My introductory comments: "The May 31 issue of PRL has a cover image of something I've been interested in for a few years, namely models of collective motion in self-propelled particles (like bird flocks), often called swarming. This paper presents a new general approach to problems like these, linking them with topics seemingly unrelated, like interferometry, genetic fitness testing, and random walks, with huge computing speed gains. I'll try to figure out what they've said and hopefully explain it."

The article abstract: "We devise powerful algorithms based on differential evolution for adaptive many-particle quantum metrology. Our new approach delivers adaptive quantum metrology policies for feedback control that are orders-of-magnitude more efficient and surpass the few-dozen-particle limitation arising in methods based on particle-swarm optimization. We apply our method to the binary-decision-tree model for quantum-enhanced phase estimation as well as to a new problem: a decision tree for adaptive estimation of the unknown bias of a quantum coin in a quantum walk and show how this latter case can be realized experimentally."