Differential Evolution for Many-Particle Adaptive Quantum Metrology

TL;DR

This paper introduces differential evolution algorithms that significantly improve adaptive quantum metrology for many particles, surpassing previous particle-swarm methods and enabling practical experimental implementations.

Contribution

The paper presents a novel differential evolution approach for adaptive quantum metrology, overcoming limitations of existing methods and enabling scalable, efficient feedback control for complex quantum systems.

Findings

Algorithms outperform particle-swarm optimization by orders of magnitude.

Effective in binary-decision-tree quantum phase estimation.

Applicable to experimental quantum walk bias estimation.

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.