An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

TL;DR

This paper presents a novel self-learning swarm-intelligence algorithm that optimizes feedback-based quantum metrology processes, capable of handling real-world imperfections to surpass the standard quantum limit.

Contribution

It introduces an efficient, adaptable algorithm for designing quantum metrology feedback procedures, improving over previous methods in robustness and practicality.

Findings

Algorithm successfully surpasses the SQL in simulations

Handles experimental imperfections and decoherence effectively

Demonstrates adaptability to real-world quantum systems

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.