Efficient flexible characterization of quantum processors with nested error models

TL;DR

This paper introduces an iterative method for efficiently characterizing quantum processors by testing nested error models against experimental data, enabling accurate modeling and comparison of quantum device errors.

Contribution

The paper presents a novel iterative technique for selecting and quantifying error models in quantum processors, improving accuracy and efficiency over existing methods.

Findings

Successfully characterized a simulated noisy 2-qubit processor

Demonstrated the effectiveness of nested error models

Provided a framework for comparing quantum processor models

Abstract

We present a simple and powerful technique for finding a good error model for a quantum processor. The technique iteratively tests a nested sequence of models against data obtained from the processor, and keeps track of the best-fit model and its wildcard error (a quantification of the unmodeled error) at each step. Each best-fit model, along with a quantification of its unmodeled error, constitute a characterization of the processor. We explain how quantum processor models can be compared with experimental data and to each other. We demonstrate the technique by using it to characterize a simulated noisy 2-qubit processor.