Benchmarking Noise Extrapolation with OpenPulse

TL;DR

This paper evaluates noise extrapolation techniques on IBM's quantum hardware, analyzing how hardware noise characteristics affect error mitigation effectiveness and identifying conditions for reliable noise extrapolation in NISQ devices.

Contribution

It introduces a hybrid benchmarking approach that incorporates control-mediated noise dependence into error mitigation analysis on real quantum hardware.

Findings

Identifies hardware regions suitable for reliable noise extrapolation.

Shows how low-level hardware characterization predicts error mitigation success.

Analyzes the impact of control fields and circuit depth on noise extrapolation.

Abstract

Distilling precise estimates from noisy intermediate scale quantum (NISQ) data has recently attracted considerable attention. In order to augment digital qubit metrics, such as gate fidelity, we discuss analog error mitigability, i.e. the ability to accurately distill precise observable estimates, as a hybrid quantum-classical computing benchmarking task. Specifically, we characterize single qubit error rates on IBM's Poughkeepsie superconducting quantum hardware, incorporate control-mediated noise dependence into a generalized rescaling protocol, and analyze how noise characteristics influence Richardson extrapolation-based error mitigation. Our results identify regions in the space of Hamiltonian control fields and circuit-depth which are most amenable to reliable noise extrapolation, as well as shedding light on how low-level hardware characterization can be used as a predictive tool for uncertainty quantification in error mitigated NISQ computations.