Sparse Non-Markovian Noise Modeling of Transmon-Based Multi-Qubit Operations

TL;DR

This paper introduces a sparse, predictive noise model for transmon-based multi-qubit quantum devices that captures non-Markovian effects and improves prediction accuracy over default models, aiding in error mitigation.

Contribution

The work presents a novel sparse noise modeling approach for multi-qubit transmon devices, effectively capturing non-Markovian effects and enhancing predictive accuracy for quantum dynamics.

Findings

Model predicts expectation values within 0.5% error

Captures non-Markovian, spatio-temporally correlated noise

Outperforms default hardware noise models by 7×

Abstract

The influence of noise on quantum dynamics is one of the main factors preventing current quantum processors from performing accurate quantum computations. Sufficient noise characterization and modeling can provide key insights into the effect of noise on quantum algorithms and inform the design of targeted error protection protocols. However, constructing effective noise models that are sparse in model parameters, yet predictive can be challenging. In this work, we present an approach for effective noise modeling of multi-qubit operations on transmon-based devices. Through a comprehensive characterization of seven devices offered by the IBM Quantum Platform, we show that the model can capture and predict a wide range of single- and two-qubit behaviors, including non-Markovian effects resulting from spatio-temporally correlated noise sources. The model's predictive power is further highlighted through multi-qubit dynamical decoupling demonstrations and an implementation of the variational quantum eigensolver. As a training proxy for the hardware, we show that the model can predict expectation values within a relative error of 0.5%; this is a 7$\times$ improvement over default hardware noise models. Through these demonstrations, we highlight key error sources in superconducting qubits and illustrate the utility of reduced noise models for predicting hardware dynamics.