Qiskit addon: approximate quantum compilation with tensor networks (AQC-Tensor)
Table of contents
About
Qiskit addons are a collection of modular tools for building utility-scale workloads powered by Qiskit.
This addon enables a Qiskit user to perform approximate quantum compilation using tensor networks, a technique that was introduced in arXiv:2301.08609.
Specifically, this package allows one to compile the initial portion of a circuit into a nearly equivalent approximation of that circuit, but with much fewer layers.
It has been tested primarily on Trotter circuits to date. It may, however, be applicable to any class of circuits where one has access to both:
- A great intermediate state, known as the "target state," that can be achieved by tensor-network simulation; and,
- A good circuit that prepares an approximation to the target state, but with fewer layers when compiled to the target hardware device.
(Figure is taken from arXiv:2301.08609.)
Documentation
All documentation is available at https://qiskit.github.io/qiskit-addon-aqc-tensor/.
Installation
We encourage installing this package via pip, when possible.
To be useful, this package requires at least one tensor-network backend. The following command installs the Qiskit Aer backend, as well as the quimb backend with automatic differentiation support from JAX:
pip install 'qiskit-addon-aqc-tensor[aer,quimb-jax]'
For more installation information refer to these installation instructions.
Deprecation Policy
We follow semantic versioning and are guided by the principles in Qiskit's deprecation policy. We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in the release notes.
Contributing
The source code is available on GitHub.
The developer guide is located at CONTRIBUTING.md in the root of this project's repository. By participating, you are expected to uphold Qiskit's code of conduct.
We use GitHub issues for tracking requests and bugs.
Citation
If you use this package in your research, please cite it according to the CITATION.bib file.
License
Metadata
Release files for qiskit-addon-aqc-tensor 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| qiskit_addon_aqc_tensor-0.3.1.tar.gz | 1.0 MB | With provenance | Details for qiskit_addon_aqc_tensor-0.3.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| qiskit_addon_aqc_tensor-0.3.1-py3-none-any.whl | Python 3 | none | any | Details for qiskit_addon_aqc_tensor-0.3.1-py3-none-any.whl |
Total release size: 1.0 MB
Release files / qiskit_addon_aqc_tensor-0.3.1.tar.gz
| Download URL | qiskit_addon_aqc_tensor-0.3.1.tar.gz |
|---|---|
| Size | 1.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
94d0d51e26cc0ec4401d997317e4910e4d74ddf1e9931aa45d51c82dd76c4f77
|
|
BLAKE2b-256 checksum How to use checksums |
1ab10349690617686a8e3d7d2cc75f6b543de5b74449818fe86e89415e5c48b3
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 16, 2026.
Transparency logRelease files / qiskit_addon_aqc_tensor-0.3.1-py3-none-any.whl
| Download URL | qiskit_addon_aqc_tensor-0.3.1-py3-none-any.whl |
|---|---|
| Size | 40.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
60c415a39888d955194714c1396806acd03b835924d6c7af85cee0b44aacb21d
|
|
BLAKE2b-256 checksum How to use checksums |
7b830e3796e057c97f7685841d6f9ce343d71252e9b6489d478ca1fdc88dc7c8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 16, 2026.
Transparency logSecurity for qiskit-addon-aqc-tensor 0.3.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 2 files in this release have been uploaded with provenance metadata.
Attestation for qiskit_addon_aqc_tensor-0.3.1.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 16, 2026.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.