Efficient learning and optimizing non-Gaussian correlated noise in digitally controlled qubit systems

TL;DR

This paper introduces a novel approach for characterizing and controlling non-Gaussian, correlated noise in quantum qubits, enabling more efficient and accurate quantum operations through higher-order spectral estimation and symmetry analysis.

Contribution

It presents a new method combining frame-based characterization and symmetry analysis to improve noise spectroscopy and control in non-Gaussian quantum environments.

Findings

Higher-order spectral estimation improves noise characterization.

Control complexity determines qubit dynamics independently of environment non-Gaussianity.

Numerical simulations demonstrate effective single and two-qubit noise control.

Abstract

Precise qubit control in the presence of spatio-temporally correlated noise is pivotal for transitioning to fault-tolerant quantum computing. Generically, such noise can also have non-Gaussian statistics, which hampers existing non-Markovian noise spectroscopy protocols. By utilizing frame-based characterization and a novel symmetry analysis, we show how to achieve higher-order spectral estimation for noise-optimized circuit design. Remarkably, we find that the digitally driven qubit dynamics can be solely determined by the complexity of the applied control, rather than the non-perturbative nature of the non-Gaussian environment. This enables us to address certain non-perturbative qubit dynamics more simply. We delineate several complexity bounds for learning such high-complexity noise and demonstrate our single and two-qubit digital characterization and control using a series of numerical simulations. Our results not only provide insights into the exact solvability of (small-sized) open quantum dynamics but also highlight a resource-efficient approach for optimal control and possible error reduction techniques for current qubit devices.