Differential Evolution for Many-Particle Adaptive Quantum Metrology

作     者：Neil B. Lovett Cécile Crosnier Martí Perarnau-Llobet Barry C. Sanders 

作者机构：Institute for Quantum Science and Technology University of Calgary Alberta T2N 1N4 Canada Département de Physique École Normale Supérieure de Cachan 94230 Cachan France Institute for Theoretical Physics University of Amsterdam 1090 GL Amsterdam The Netherlands ICFO-Institut de Ciencies Fotoniques Mediterranean Technology Park 08860 Castelldefels (Barcelona) Spain 

出 版 物：《Physical Review Letters》 (Phys Rev Lett)

年 卷 期：2013年第110卷第22期

页      面：220501-220501页

核心收录：

学科分类：07[理学] 0702[理学-物理学] 

基　　金：CIFAR NSERC AITF MPRIME La Caixa Foundation 

主　　题：DIFFERENTIAL evolution (Computer science) QUANTUM theory METROLOGY PARTICLE swarm optimization COMPUTER algorithms 

摘      要：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.