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Co-authored-by: Jen Glick <41485571+jenglick@users.noreply.github.com>
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docs/guides/qiskit-addons.mdx
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@ -7,7 +7,7 @@ description: Understand the Qiskit addon tools, which help you build utility-gra
Qiskit addons are a collection of research capabilities for enabling algorithm discovery at the utility scale. These capabilities build on Qiskit's performant foundation of tools for creating and running quantum algorithms. They are provided as modular software components that can plug into a [workflow](/guides/intro-to-patterns) to scale or design new quantum algorithms. Qiskit addons are a collection of research capabilities for enabling algorithm discovery at the utility scale. These capabilities build on Qiskit's performant foundation of tools for creating and running quantum algorithms. They are provided as modular software components that can plug into a [workflow](/guides/intro-to-patterns) to scale or design new quantum algorithms.
## Mapping addons ## Addons for mapping
### Multi-product formulas ### Multi-product formulas
@ -16,7 +16,7 @@ Multi-product formulas (MPF) reduce the Trotter error of Hamiltonian dynamics th
- Visit the [GitHub repository.](https://github.com/Qiskit/qiskit-addon-mpf) - Visit the [GitHub repository.](https://github.com/Qiskit/qiskit-addon-mpf)
- Read the [documentation.](https://qiskit.github.io/qiskit-addon-mpf) - Read the [documentation.](https://qiskit.github.io/qiskit-addon-mpf)
## Optimizing addons ## Addons for optimizing
### Operator backpropagation ### Operator backpropagation
@ -32,7 +32,7 @@ Circuit cutting reduces the depth of transpiled circuits by decomposing entangli
- Visit the [GitHub repository.](https://github.com/Qiskit/qiskit-addon-cutting) - Visit the [GitHub repository.](https://github.com/Qiskit/qiskit-addon-cutting)
- Read the [documentation.](https://qiskit.github.io/qiskit-addon-cutting) - Read the [documentation.](https://qiskit.github.io/qiskit-addon-cutting)
## Post-processing addons ## Addons for post-processing
### Sample-based quantum diagonalization ### Sample-based quantum diagonalization