--- jupytext: text_representation: extension: .md format_name: myst format_version: 0.13 jupytext_version: 1.10.3 kernelspec: display_name: Python 3 (ipykernel) language: python name: python3 --- # When should I use ZNE? ## Advantages Zero noise extrapolation is one of the simplest error mitigation techniques and, in many practical situations, it can be applied with a relatively small sampling cost. The main advantage of ZNE is that the technique can be applied without a detailed knowledge of the undelying noise model. Therefore it can be a good option in situations where tomography is impractical. ## Disadvantages In some instances the results of the extrapolation can exhibit a large bias {cite}`Mari_2021_PRA`. ZNE may not be helpful in cases where a low degree polynomial curve obtained by fitting the noisy expectation values does not match the zero-noise limit. When using circuits of less trivial depth on real devices, the lowest error points may be too noisy for the extrapolation to show improvement over the unmitigated result {cite}`Lowe_2021_PRR`. ## Example For a simple example in which the application of ZNE reduces the estimation error when compared to the unmitigated result, see: [Zero Noise Extrapolation for mitigating errors in energy landscape of a two-qubit variational circuit](https://mitiq.readthedocs.io/en/latest/examples/simple-landscape-cirq.html)