Efficient adaptive Bayesian estimation of a slowly fluctuating Overhauser field gradient

TL;DR

This paper introduces two efficient adaptive Bayesian methods for real-time estimation of slowly fluctuating Overhauser field gradients in spin qubits, improving accuracy over traditional methods while considering dephasing and drift effects.

Contribution

The paper presents novel adaptive Bayesian schemes tailored for real-time estimation of Overhauser field gradients in quantum dot spin qubits, emphasizing computational efficiency and accuracy.

Findings

Significant improvement in estimation accuracy over traditional methods.

Feasible real-time implementation with reduced computational overhead.

Effective handling of dephasing and gradient drift effects.

Abstract

Slow fluctuations of Overhauser fields are an important source for decoherence in spin qubits hosted in III-V semiconductor quantum dots. Focusing on the effect of the field gradient on double-dot singlet-triplet qubits, we present two adaptive Bayesian schemes to estimate the magnitude of the gradient by a series of free induction decay experiments. We concentrate on reducing the computational overhead, with a real-time implementation of the schemes in mind. We show how it is possible to achieve a significant improvement of estimation accuracy compared to more traditional estimation methods. We include an analysis of the effects of dephasing and the drift of the gradient itself.