Benchmarking Characterization Methods for Noisy Quantum Circuits

TL;DR

This paper benchmarks various noise characterization methods for a 27-qubit superconducting quantum device, finding empirical direct characterization most scalable and accurate, with the choice of method depending on the circuit.

Contribution

It provides a comparative analysis of noise characterization techniques, highlighting empirical direct characterization as the most scalable and accurate method for complex quantum circuits.

Findings

Empirical direct characterization scales best among tested methods.

Noise model accuracy does not correlate with information gained from characterization.

The optimal characterization method depends on the specific quantum circuit.

Abstract

Effective methods for characterizing the noise in quantum computing devices are essential for programming and debugging circuit performance. Existing approaches vary in the information obtained as well as the amount of quantum and classical resources required, with more information generally requiring more resources. Here we benchmark the characterization methods of gate set tomography, Pauli channel noise reconstruction, and empirical direct characterization for developing models that describe noisy quantum circuit performance on a 27-qubit superconducting transmon device. We evaluate these models by comparing the accuracy of noisy circuit simulations with the corresponding experimental observations. We find that the agreement of noise model to experiment does not correlate with the information gained by characterization and that the underlying circuit strongly influences the best choice of characterization approach. Empirical direct characterization scales best of the methods we tested and produced the most accurate characterizations across our benchmarks.