---
title: Noise Modeling
description: Overview of packages used for building noise models
source: https://quantum.cloud.ibm.com/docs/en/guides/noise-modeling
---

# Noise Modeling

A noise model describes how errors affect the results from executing a quantum circuit. Noise models support several stages of a typical workflow including: simulating a circuit classically to estimate expected performance before submitting a job  or to inform an error-mitigation technique such as probabilistic error cancellation. For general strategies to reduce the impact of noise during execution, see [Overview of noise management techniques](/docs/guides/error-mitigation-and-suppression-techniques).

This page collects the packages and capabilities that support each part of that workflow, grouped into learning noise from a device, building a noise model for classical simulation, and refining a noise model for a specific error-mitigation technique.

## Building and running noise learning algorithms

Use noise learning packages to characterize a device's noise directly from experimental data. These packages run randomized circuits to estimate error rates and build a noise model that reflects actual hardware behavior.

- [**Qiskit noise learning**](/docs/addons/qiskit-noise-learning) — Build randomization-based quantum noise characterization experiments and model-fitting analysis pipelines
- [**NoiseLearner for IBM Quantum Compute**](/docs/ibm-quantum-compute/noise-learning) — Construct noise models for PEC and PEA workloads using a target device with the IBM Quantum Compute service.

## Building noise models for noisy circuit simulation

Use this package to construct a noise model for simulating circuits classically. With a noise model, you can estimate how a circuit behaves on noisy hardware before you run it on a QPU.

- [**Build noise model with Qiskit Aer**](/docs/qiskit/build-noise-models) — Build noise model with Qiskit Aer.

## Refining noise models

Use these packages to adjust a noise model for a specific error-mitigation technique. Each package targets a different tradeoff between sampling cost, bias, and the reliability of postselected samples.

- [**Lightcone shading for PEC**](/docs/addons/qiskit-addon-slc) — Reduce probabilistic error cancellation (PEC) sampling overhead by removing noise model terms that have low impact on the observable estimation.
- [**Post-selected logical noise models for PEC**](/docs/addons/qiskit-mitigation/guides/postselection-with-non-markovian-error-checks) — Reduce probabilistic error cancellation (PEC) sampling overhead by removing noise model terms that can be detected by error checks in the circuit.