2023/06/23 by Zhichao Peng, Peng, Zhichao, Daniel Appelö +9
Computer Science · Physics and Astronomy · #62F15 #81P68 #Advanced Thermodynamics and Statistical Mechanics #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Model Reduction and Neural Networks #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2306.13747
openalex publication_date 2023/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.