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arXiv · 1304.2246

Differential Evolution for Many-Particle Adaptive Quantum Metrology

Neil B. Lovett·Cécile Crosnier·Martí Perarnau-Llobet·Barry C. Sanders
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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.

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Neil B. Lovett, Cécile Crosnier, Martí Perarnau-Llobet, Barry C. Sanders. 2013-06-01. Differential Evolution for Many-Particle Adaptive Quantum Metrology. https://doi.org/10.1103/physrevlett.110.220501

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