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## Description
This PR adds support information to the README and the documentation.
For more information, see
munich-quantum-toolkit/.github#49.
## Checklist:
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relevant to this change.
- [x] ~I have added appropriate tests that cover the new/changed
functionality.~
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additions, changes, fixes, or removals.~
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needed).~
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new warnings.
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
# MQT Predictor: Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing
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# MQT Predictor - Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing
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MQT Predictor is a framework that allows one to automatically select a suitable quantum device for a particular application and provides an optimized compiler for the selected device.
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It not only supports end-users in navigating the vast landscape of choices, it also allows to mix-and-match compiler passes from various tools to create optimized compilers that transcend the individual tools.
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It is part of the [_Munich Quantum Toolkit (MQT)_](https://mqt.readthedocs.io).
MQT Predictor supports end-users in navigating the vast landscape of choices by allowing them to mix-and-match compiler passes from various tools to create optimized compilers that transcend the individual tools.
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Evaluations on more than 500 quantum circuits and seven devices have shown that—compared to Qiskit's and TKET's most optimized compilation flows—the MQT Predictor yields circuits with an expected fidelity that is on par with the best possible result that could be achieved by trying out all combinations of devices and compilers and even achieves a similar performance when considering the critical depth as an alternative figure of merit.
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Therefore, MQT Predictor tackles this problem from two angles:
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1. It provides a method (based on Reinforcement Learning) that produces device-specific quantum circuit compilers by combining compilation passes from various compiler tools and learning optimized sequences of those passes with respect to a customizable figure of merit). This mix-and-match of compiler passes from various tools allows one to eliminate vendor locks and to create optimized compilers that transcend the individual tools.
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1. It provides a method (based on Reinforcement Learning) that produces device-specific quantum circuit compilers by combining compilation passes from various compiler tools and learning optimized sequences of those passes with respect to a customizable figure of merit.
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This mix-and-match of compiler passes from various tools allows one to eliminate vendor locks and to create optimized compilers that transcend the individual tools.
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2. It provides a prediction method (based on Supervised Machine Learning) that, without performing any compilation, automatically predicts the most suitable device for a given application. This completely eliminates the manual and laborious task of determining a suitable target device and guides end-users through the vast landscape of choices without the need for quantum computing expertise.
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2. It provides a prediction method (based on Supervised Machine Learning) that, without performing any compilation, automatically predicts the most suitable device for a given application.
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This completely eliminates the manual and laborious task of determining a suitable target device and guides end-users through the vast landscape of choices without the need for quantum computing expertise.
If you have any questions, feel free to create a [discussion](https://github.com/munich-quantum-toolkit/predictor/discussions) or an [issue](https://github.com/munich-quantum-toolkit/predictor/issues) on [GitHub](https://github.com/munich-quantum-toolkit/predictor).
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## Contributors and Supporters
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<imgwidth=20%src="https://img.shields.io/badge/Sponsor-white?style=for-the-badge&logo=githubsponsors&labelColor=black&color=blue"alt="Sponsor the MQT" />
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</a>
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</p>
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## Getting Started
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`mqt.predictor` is available via [PyPI](https://pypi.org/project/mqt.predictor/).
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```console
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(venv) $ pip install mqt.predictor
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(.venv) $ pip install mqt.predictor
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```
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The following code gives an example on the usage:
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from mqt.predictor import qcompile
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from mqt.bench import get_benchmark, BenchmarkLevel
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#get a benchmark circuit on algorithmic level representing the GHZ state with 5 qubits from [MQT Bench](https://github.com/munich-quantum-toolkit/bench)
#print the selected device and the compilation information
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#Print the selected device and the compilation information
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print(quantum_device, compilation_information)
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#draw the compiled circuit
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#Draw the compiled circuit
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print(qc_compiled.draw())
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```
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> [!NOTE]
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> To execute the code, respective machine learning models must be trained before.
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> Up until mqt.predictor v2.0.0, pre-trained models were provided. However, this is not feasible anymore due to the
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> increasing number of devices and figures of merits. Instead, we now provide a detailed documentation on how to train
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> and setup the MQT Predictor framework.\*\*
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> Up until mqt.predictor v2.0.0, pre-trained models were provided.
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> However, this is not feasible anymore due to the increasing number of devices and figures of merits.
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> Instead, we now provide a detailed documentation on how to train and setup the MQT Predictor framework.
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**Detailed documentation and examples are available at [ReadTheDocs](https://mqt.readthedocs.io/projects/predictor).**
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## System Requirements
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**Further documentation and examples are available at [ReadTheDocs](https://mqt.readthedocs.io/projects/predictor).**
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MQT Predictor can be installed on all major operating systems with all supported Python versions.
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Building (and running) is continuously tested under Linux, macOS, and Windows using the [latest available system versions for GitHub Actions](https://github.com/actions/runner-images).
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## Cite This
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Please cite the work that best fits your use case.
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### MQT Predictor (the tool)
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When citing the software itself or results produced with it, cite the MQT Predictor paper:
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```bibtex
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@article{quetschlich2025mqtpredictor,
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title = {{MQT Predictor: Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing}},
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author = {Quetschlich, Nils and Burgholzer, Lukas and Wille, Robert},
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year = {2025},
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journal = {ACM Transactions on Quantum Computing (TQC)},
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doi = {10.1145/3673241},
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eprint = {2310.06889},
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eprinttype = {arxiv}
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}
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```
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##References
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### The Munich Quantum Toolkit (the project)
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In case you are using MQT Predictor in your work, we would be thankful if you referred to it by citing the following publication:
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When discussing the overall MQT project or its ecosystem, cite the MQT Handbook:
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```bibtex
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@ARTICLE{quetschlich2025mqtpredictor,
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AUTHOR = {N. Quetschlich and L. Burgholzer and R. Wille},
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TITLE = {{MQT Predictor: Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing}},
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YEAR = {2025},
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JOURNAL = {ACM Transactions on Quantum Computing (TQC)},
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DOI = {10.1145/3673241},
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EPRINT = {2310.06889},
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EPRINTTYPE = {arxiv},
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@inproceedings{mqt,
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title = {The {{MQT}} Handbook: {{A}} Summary of Design Automation Tools and Software for Quantum Computing},
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shorttitle = {{The MQT Handbook}},
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author = {Wille, Robert and Berent, Lucas and Forster, Tobias and Kunasaikaran, Jagatheesan and Mato, Kevin and Peham, Tom and Quetschlich, Nils and Rovara, Damian and Sander, Aaron and Schmid, Ludwig and Schoenberger, Daniel and Stade, Yannick and Burgholzer, Lukas},
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year = 2024,
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booktitle = {IEEE International Conference on Quantum Software (QSW)},
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doi = {10.1109/QSW62656.2024.00013},
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eprint = {2405.17543},
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eprinttype = {arxiv},
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addendum = {A live version of this document is available at \url{https://mqt.readthedocs.io}}
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}
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```
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---
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## Acknowledgements
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This project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research
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and innovation program (grant agreement No. 101001318), was part of the Munich Quantum Valley, which is supported by the
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Bavarian state government with funds from the Hightech Agenda Bayern Plus, and has been supported by the BMWK on the
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basis of a decision by the German Bundestag through project QuaST, as well as by the BMK, BMDW, the State of Upper
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Austria in the frame of the COMET program, and the QuantumReady project within Quantum Austria (managed by the FFG).
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The Munich Quantum Toolkit has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 101001318), the Bavarian State Ministry for Science and Arts through the Distinguished Professorship Program, as well as the Munich Quantum Valley, which is supported by the Bavarian state government with funds from the Hightech Agenda Bayern Plus.
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# Welcome to MQT Predictor's documentation!
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MQT Predictor is a tool for Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing developed as part of the [Munich Quantum Toolkit](https://mqt.readthedocs.io) (_MQT_).
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MQT Predictor is a tool for automatic device selection with device-specific circuit compilation for quantum computing.
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It is part of the _{doc}`Munich Quantum Toolkit (MQT) <mqt:index>`_.
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From a user's perspective, the framework works as follows:
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The MQT Predictor framework is based on two main components:
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- An [Automatic Device Selection](device_selection.md) component that predicts the most suitable device for a given quantum circuit and figure of merit.
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- A [Device-Specific Circuit Compilation](compilation.md) component that compiles a given quantum circuit for a given device.
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- An {doc}`Automatic Device Selection <device_selection>` component that predicts the most suitable device for a given quantum circuit and figure of merit.
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- A {doc}`Device-Specific Circuit Compilation <compilation>` component that compiles a given quantum circuit for a given device.
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Combining these two components, the framework can be used to automatically compile a given quantum circuit for the most suitable device optimizing a [customizable figure of merit](figure_of_merit.md).
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How to install the framework is described in the [installation](installation.md) section, how to set it up in the [setup](setup.md) section, and how to use it in the [quickstart](quickstart.md) section.
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Combining these two components, the framework can be used to automatically compile a given quantum circuit for the most suitable device optimizing a {doc}`customizable figure of merit <figure_of_merit>`.
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How to install the framework is described in the {doc}`installation section <installation>`, how to set it up in the {doc}`setup section <setup>` section, and how to use it in the {doc}`quickstart section <quickstart>` section.
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If you are interested in the theory behind MQT Predictor, have a look at the publications in the [references list](references.md).
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If you are interested in the theory behind MQT Predictor, have a look at the publications in the {doc}`references list <references>`.
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---
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```{toctree}
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:hidden: true
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:hidden:
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self
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```
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```{toctree}
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:caption: User Guide
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:glob: true
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:glob:
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:hidden:
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:maxdepth: 1
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installation
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```{toctree}
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:caption: Developers
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:glob: true
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:glob:
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:hidden:
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:maxdepth: 1
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contributing
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development_guide
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support
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```
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````{only} html
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```{only} html
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## Contributors and Supporters
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The _[Munich Quantum Toolkit (MQT)](https://mqt.readthedocs.io)_ is developed by the [Chair for Design Automation](https://www.cda.cit.tum.de/) at the [Technical University of Munich](https://www.tum.de/)
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and supported by the [Munich Quantum Software Company (MQSC)](https://munichquantum.software).
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Among others, it is part of the [Munich Quantum Software Stack (MQSS)](https://www.munich-quantum-valley.de/research/research-areas/mqss) ecosystem,
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which is being developed as part of the [Munich Quantum Valley (MQV)](https://www.munich-quantum-valley.de) initiative.
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The _[Munich Quantum Toolkit (MQT)](https://mqt.readthedocs.io)_ is developed by the [Chair for Design Automation](https://www.cda.cit.tum.de/) at the [Technical University of Munich](https://www.tum.de/) and supported by the [Munich Quantum Software Company (MQSC)](https://munichquantum.software).
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Among others, it is part of the [Munich Quantum Software Stack (MQSS)](https://www.munich-quantum-valley.de/research/research-areas/mqss) ecosystem, which is being developed as part of the [Munich Quantum Valley (MQV)](https://www.munich-quantum-valley.de) initiative.
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