A tool for quantum circuit compilation developed as part of the Munich Quantum Toolkit (MQT). It builds upon MQT Core, which forms the backbone of the MQT.
- Exact and heuristic circuit mapping to superconducting devices: gate-optimal MaxSAT/SMT-based mapping with Z3 for small circuits and scalable A*-search–based mapping for larger ones. Guide • Exact • Heuristic
- Clifford circuit synthesis and optimization: SAT-based depth/gate-optimal Clifford synthesis with optional destabilizer preservation, plus a fast heuristic splitter for larger circuits. Guide
- Zoned neutral-atom compilers: routing-agnostic and routing-aware flows that place, route, and schedule atom transfers between storage/entanglement zones. Guide
- Neutral-atom logical state preparation (NASP): SMT-based generator for optimal preparation schedules of logical graph states on zoned architectures. Guide
- Hybrid circuit mapper for neutral atom quantum computers: a hybrid approach combining superconducting mapping techniques with atom shuttling.
- Python-first API with Qiskit integration: pass
QuantumCircuitor OpenQASM; one-callcompile()oroptimize_clifford()via plugin wrappers. API - Efficient and portable: C++20 core with Z3-backed solvers, prebuilt wheels for Linux/macOS/Windows via PyPI.
If you have any questions, feel free to create a discussion or an issue on GitHub.
The Munich Quantum Toolkit (MQT) is developed by the Chair for Design Automation at the Technical University of Munich and supported by the Munich Quantum Software Company (MQSC). Among others, it is part of the Munich Quantum Software Stack (MQSS) ecosystem, which is being developed as part of the Munich Quantum Valley (MQV) initiative.
Thank you to all the contributors who have helped make MQT QMAP a reality!
The MQT will remain free, open-source, and permissively licensed—now and in the future. We are firmly committed to keeping it open and actively maintained for the quantum computing community.
To support this endeavor, please consider:
- Starring and sharing our repositories: https://github.com/munich-quantum-toolkit
- Contributing code, documentation, tests, or examples via issues and pull requests
- Citing the MQT in your publications (see Cite This)
- Citing our research in your publications (see References)
- Using the MQT in research and teaching, and sharing feedback and use cases
- Sponsoring us on GitHub: https://github.com/sponsors/munich-quantum-toolkit
MQT QMAP is available via PyPI for Linux, macOS, and Windows and supports Python 3.9 to 3.14.
(venv) $ pip install mqt.qmapCompiling a given quantum circuit to a certain device is as easy as
from mqt.qmap.plugins.qiskit.sc import compile
from qiskit import QuantumCircuit
from qiskit.providers.fake_provider import GenericBackendV2
circ = QuantumCircuit(3)
circ.h(0)
circ.cx(0, 1)
circ.cx(0, 2)
arch = GenericBackendV2(
num_qubits=5,
coupling_map=[[0, 1], [1, 0], [1, 2], [2, 1], [1, 3], [3, 1], [3, 4], [4, 3]],
)
circ_mapped, results = compile(circ, arch=arch)Optimizing a Clifford circuit is as easy as
from mqt.qmap.plugins.qiskit.clifford_synthesis import optimize_clifford
from qiskit import QuantumCircuit
circ = QuantumCircuit(2)
circ.h(1)
circ.cx(0, 1)
circ.h(0)
circ.h(1)
circ_opt, results = optimize_clifford(circ)Detailed documentation on all available methods, options, and input formats is available at ReadTheDocs.
The implementation is compatible with any C++20 compiler, a minimum CMake version of 3.24, and Python 3.9+. Please refer to the documentation on how to build the project.
Building (and running) is continuously tested under Linux, macOS, and Windows using the latest available system versions for GitHub Actions.
Please cite the work that best fits your use case.
When citing the software itself or results produced with it, cite the MQT QMAP paper:
@inproceedings{wille2023qmap,
title = {{{MQT QMAP}}: {{Efficient}} quantum circuit mapping},
author = {Wille, Robert and Burgholzer, Lukas},
year = 2023,
booktitle = {International Symp. on Physical Design},
doi = {10.1145/3569052.3578928}
}When discussing the overall MQT project or its ecosystem, cite the MQT Handbook:
@inproceedings{mqt,
title = {The {{MQT}} Handbook: {{A}} Summary of Design Automation Tools and Software for Quantum Computing},
shorttitle = {{The MQT Handbook}},
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},
year = 2024,
booktitle = {IEEE International Conference on Quantum Software (QSW)},
doi = {10.1109/QSW62656.2024.00013},
eprint = {2405.17543},
eprinttype = {arxiv},
addendum = {A live version of this document is available at \url{https://mqt.readthedocs.io}}
}When citing the underlying methods and research, please reference the most relevant peer-reviewed publications from the list below:
[1] R. Wille and L. Burgholzer. MQT QMAP: Efficient Quantum Circuit Mapping. In International Symposium on Physical Design (ISPD), 2023.
[2] A. Zulehner, A. Paler, and R. Wille. An Efficient Methodology for Mapping Quantum Circuits to the IBM QX Architectures. IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems (TCAD), 2018.
[3] R. Wille, L. Burgholzer, and A. Zulehner. Mapping Quantum Circuits to IBM QX Architectures Using the Minimal Number of SWAP and H Operations. In Design Automation Conference (DAC), 2019.
[4] S. Hillmich, A. Zulehner, and R. Wille. Exploiting Quantum Teleportation in Quantum Circuit Mapping. In Asia and South Pacific Design Automation Conference (ASP-DAC), 2021.
[5] L. Burgholzer, S. Schneider, and R. Wille. Limiting the Search Space in Optimal Quantum Circuit Mapping. In Asia and South Pacific Design Automation Conference (ASP-DAC), 2022.
[6] T. Peham, L. Burgholzer, and R. Wille. On Optimal Subarchitectures for Quantum Circuit Mapping. ACM Transactions on Quantum Computing (TQC), 2023.
[7] S. Schneider, L. Burgholzer, and R. Wille. A SAT Encoding for Optimal Clifford Circuit Synthesis. In Asia and South Pacific Design Automation Conference (ASP-DAC), 2023.
[8] T. Peham, N. Brandl, R. Kueng, R. Wille, and L. Burgholzer. Depth-Optimal Synthesis of Clifford Circuits with SAT Solvers. In IEEE International Conference on Quantum Computing and Engineering (QCE), 2023.
[9] L. Schmid, D. F. Locher, M. Rispler, S. Blatt, J. Zeiher, M. Müller, and R. Wille. Computational Capabilities and Compiler Development for Neutral Atom Quantum Processors: Connecting Tool Developers and Hardware Experts. Quantum Science and Technology, 2024.
[10] L. Schmid, S. Park, S. Kang, and R. Wille. Hybrid Circuit Mapping: Leveraging the Full Spectrum of Computational Capabilities of Neutral Atom Quantum Computers. In Design Automation Conference (DAC), 2024.
[11] Y. Stade, L. Schmid, L. Burgholzer, and R. Wille. An Abstract Model and Efficient Routing for Logical Entangling Gates on Zoned Neutral Atom Architectures. In Int'l Conf. on Quantum Computing and Engineering, 2024.
[12] Y. Stade, L. Schmid, L. Burgholzer, and R. Wille. Optimal State Preparation for Logical Arrays on Zoned Neutral Atom Quantum Computers. In Design, Automation and Test in Europe, 2024.
[13] Y. Stade, W.-H. Lin, J. Cong, and R. Wille. Routing-Aware Placement for Zoned Neutral Atom-based Quantum Computing. In Int'l Conference on CAD, 2025.
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.
