Deterministic and Bayesian Characterization of Quantum Computing Devices

TL;DR

This paper introduces a data-driven method combining deterministic and Bayesian techniques to characterize superconducting quantum devices, improving parameter estimation amidst noise and fluctuations.

Contribution

It presents a novel approach that integrates deterministic estimates with Bayesian inference for accurate quantum device modeling.

Findings

Accurately captures experimental data with a Lindbladian model.

Provides posterior distributions for transition frequencies.

Demonstrates applicability on real superconducting qubits.

Abstract

Motivated by the noisy and fluctuating behavior of current quantum computing devices, this paper presents a data-driven characterization approach for estimating transition frequencies and decay times in a Lindbladian dynamical model of a superconducting quantum device. The data includes parity events in the transition frequency between the first and second excited states. A simple but effective mathematical model, based upon averaging solutions of two Lindbladian models, is demonstrated to accurately capture the experimental observations. A deterministic point estimate of the device parameters is first performed to minimize the misfit between data and Lindbladian simulations. These estimates are used to make an informed choice of prior distributions for the subsequent Bayesian inference. An additive Gaussian noise model is developed for the likelihood function, which includes two hyper-parameters to capture the noise structure of the data. The outcome of the Bayesian inference are posterior probability distributions of the transition frequencies, which for example can be utilized to design risk neutral optimal control pulses. The applicability of our approach is demonstrated on experimental data from the Quantum Device and Integration Testbed (QuDIT) at Lawrence Livermore National Laboratory, using a tantalum-based superconducting transmon device.