Role of the filter functions in noise spectroscopy

TL;DR

This paper advances quantum noise spectroscopy by formalizing the filter orthogonalization method within a minimum mean squared error framework, introducing a non-negative least squares approach, and proposing a new frequency domain filter design protocol.

Contribution

It formalizes the filter orthogonalization method, introduces a non-negative least squares estimator, and develops a novel frequency domain filter design protocol for improved noise spectral density reconstruction.

Findings

Filter orthogonalization is equivalent to minimum mean squared error estimation.

Non-negative least squares ensures physically meaningful spectral density estimates.

Numerical tests demonstrate improved reconstruction of complex noise spectra.

Abstract

The success of quantum noise sensing methods depends on the optimal interplay between properly designed control pulses and statistically informative measurement data on a specific quantum-probe observable. To enhance the information content of the data and reduce as much as possible the number of measurements on the probe, the filter orthogonalization method has been recently introduced. The latter is able to transform the control filter functions on an orthogonal basis allowing for the optimal reconstruction of the noise power spectral density. In this paper, we formalize this method within the standard formalism of minimum mean squared error estimation and we show the equivalence between the solutions of the two approaches. Then, we introduce a non-negative least squares formulation that ensures the non-negativeness of the estimated noise spectral density. Moreover, we also propose a novel protocol for the design in the frequency domain of the set of filter functions. The frequency-designed filter functions and the non-negative least squares reconstruction are numerically tested on noise spectra with multiple components and as a function of the estimation parameters.