Optimizing fingerprinting experiments for parameter identification: Application to spin systems

TL;DR

This paper presents the Optimal Fingerprinting Process, combining optimal control and curve fitting, to improve parameter identification in physical systems, demonstrated on spin-1/2 particles with results aligning well with theory.

Contribution

It introduces a novel optimal fingerprinting method that enhances parameter estimation accuracy using optimal control and curve fitting techniques.

Findings

The method accurately estimates spin relaxation parameters.

Experimental results agree with theoretical predictions.

Identifies physical limits of parameter estimation.

Abstract

We introduce the Optimal Fingerprinting Process which is aimed at accurately identifying the parameters which characterize the dynamics of a physical system. A database is first built from the time evolution of an ensemble of dynamical systems driven by a specific field, which is designed by optimal control theory to maximize the efficiency of the recognition process. Curve fitting is then applied to enhance the precision of the identification. As an illustrative example, we consider the estimation of the relaxation parameters of a spin- 1/2 particle. The experimental results are in good accordance with the theoretical computations. We show on this example a physical limit of the estimation process.