Optimally band-limited spectroscopy of control noise using a qubit sensor

TL;DR

This paper develops a quantum multitaper spectral estimation framework using band-limited Slepian modulation to accurately characterize control noise in qubits, improving resolution and reducing bias compared to existing methods.

Contribution

It introduces a quantum extension of classical multitaper spectral estimation, including adaptive and single-setting approaches, for enhanced noise spectral analysis in qubit sensors.

Findings

Multitaper methods outperform dynamical decoupling in spectral leakage reduction.

Band-limited Slepian modulation improves spectral resolution.

Quantum multitaper estimation accurately reconstructs spectral densities.

Abstract

Classical control noise is ubiquitous in qubit devices, making its accurate spectral characterization essential for designing optimized error suppression strategies at the physical level. Here, we focus on multiplicative Gaussian amplitude control noise on a driven qubit sensor and show that sensing protocols using optimally band-limited Slepian modulation offer substantial benefit in realistic scenarios. Special emphasis is given to laying out the theoretical framework necessary for extending non-parametric multitaper spectral estimation to the quantum setting by highlighting key points of contact and differences with respect to the classical formulation. In particular, we introduce and analyze two approaches (adaptive vs. single-setting) to quantum multitaper estimation, and show how they provide a practical means to both identify fine spectral features not otherwise detectable by existing protocols and to obtain reliable prior estimates for use in subsequent parametric estimation, including high-resolution Bayesian techniques. We quantitatively characterize the performance of both single- and multitaper Slepian estimation protocols by numerically reconstructing representative spectral densities, and demonstrate their advantage over dynamical-decoupling noise spectroscopy approaches in reducing bias from spectral leakage as well as in compensating for aliasing effects while maintaining a desired sampling resolution.