---
title: Advanced techniques - Qiskit addons
description: Overview of all Qiskit addons with links to more details
source: https://eu-de.quantum.cloud.ibm.com/docs/en/guides/addons
---

# Advanced techniques - Qiskit addons

Qiskit addons are a collection of research capabilities for enabling algorithm discovery at the utility scale. These modular software components build on Qiskit’s performant foundation and can plug into a [workflow](/docs/guides/intro-to-patterns) to scale or design new quantum algorithms. This page highlights the available tools across key functional categories to help you choose relevant capabilities when building your workflows.

## Map domain problems

These capabilities specialize in mapping domain problems into quantum operators and circuits for execution on a quantum computer.

- [**Optimization mapper**](https://qiskit.github.io/qiskit-addon-opt-mapper/) — Model optimization problems and map them into representations that can be understood by a quantum computer.
- [**Fermionic mapper**](https://qiskit.github.io/qiskit-fermions/) — Model fermionic quantum systems and map them to qubit operators and circuits.
- [**AQC-Tensor**](https://qiskit.github.io/qiskit-addon-aqc-tensor/) — Construct high-fidelity circuits with reduced depth using approximate quantum compilation with tensor networks.
- [**Multi-product formulas**](https://qiskit.github.io/qiskit-addon-mpf/) — Reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions.

## Optimize circuits for hardware execution

These capabilities are useful for reducing circuit depth and typically come with an increased sampling overhead.

- [**Operator backpropagation**](/docs/addons/qiskit-addon-obp) — Reduce circuit depth in expectation value estimations by backpropagating observables through the circuit.
- [**Circuit cutting**](https://qiskit.github.io/qiskit-addon-cutting) — Reduce the depth of transpiled circuits by decomposing entangling gates between non-adjacent qubits.

## Manage noise for expectation value estimation

Use the following addons to manage noise when building quantum workloads that estimate expectation values of observables.

- [**Propagated noise absorption**](/docs/addons/qiskit-addon-pna) — Mitigate expectation values by measuring a target observable that has absorbed noise model information.
- [**Shaded lightcones**](/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.

## Manage noise for sampling results

These techniques are useful for managing noise on sampling results.

- [**Sample-based quantum diagonalization**](/docs/addons/qiskit-addon-sqd) — Estimate the spectrum of quantum Hamiltonians by processing noisy samples and diagonalizing in a reduced subspace.
- [**SQD for HPC**](https://qiskit.github.io/qiskit-addon-sqd-hpc/) — An HPC-ready implementation of the SQD addon, written in modern C++17 standards and designed to enable HPC workflows and applications.
- [**Postselection with bit-flip checks**](https://github.com/Qiskit/qiskit-addon-utils/blob/main/docs/how_tos/postselection_with_bit_flip_checks.ipynb) — Filter out non-Markovian noise in sampled bitstrings by incorporating bit-flip checks into a circuit.
- [**Matrix-free Measurement Mitigation (M3)**](https://qiskit.github.io/qiskit-addon-mthree/) — Mitigate measurement errors by processing in a reduced subspace defined by noisy bitstrings.
- [**Paulice**](/docs/addons/qiskit-addon-paulice) — Boost the fidelity of sampled distributions using hardware-efficient Pauli checks based on spacetime codes.

## Supporting capabilities

Use these capabilities to support and compose your workflows that leverage other addons.

- [**Addon utilities**](https://qiskit.github.io/qiskit-addon-utils/) — Build addons-powered workflows faster by using this collection of functions for creating Hamiltonians, generating Trotter time-evolution circuits, and applying the latest error mitigation capabilities.
- [**Pauli propagation**](/docs/addons/pauli-prop/) — A framework to approximate operator evolution, which can be used to simulate expectation values and implement error mitigation techniques, such as propagated noise absorption (PNA) and shaded lightcones.