Simultaneous model selection and parameter estimation: A superconducting qubit coupled to a bath of incoherent two-level systems

TL;DR

This paper introduces a method for simultaneously selecting models and estimating parameters in quantum system characterization, improving efficiency by adapting measurement settings during data collection.

Contribution

It presents a novel adaptive approach that combines model selection with parameter estimation, applicable to superconducting qubits and their decoherence mechanisms.

Findings

Enhanced measurement efficiency in quantum system characterization

Successful application to superconducting qubits and two-level systems

Improved understanding of decoherence processes

Abstract

In characterization of quantum systems, adapting measurement settings based on data while it is collected can generally outperform in efficiency conventional measurements that are carried out independently of data. The existing methods for choosing measurement settings adaptively assume that the model, or the number of unknown parameters, is known. We introduce simultaneous adaptive model selection and parameter estimation. We apply our technique for characterization of a superconducting qubit and a bath of incoherent two-level systems, a leading decoherence mechanism in the state-of-the-art superconducting qubits.