MQT QMAP - A tool for Quantum Circuit Compilation

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.

Key Features

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. Guide
Python-first API with Qiskit integration: pass QuantumCircuit or OpenQASM; one-call compile() or optimize_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.

Contributors and Supporters

The Munich Quantum Toolkit (MQT) is developed by the Chair for Design Automation at the Technical University of Munich and supported by 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.

MQT Partner Logos

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

Getting Started

MQT QMAP is available via PyPI.

uv pip install mqt.qmap

Compiling 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 and examples are available at ReadTheDocs.

System Requirements and Building

Building the project requires a C++ compiler with support for C++20 and CMake 3.24 or newer. For details on how to build the project, please refer to the documentation. Building (and running) is continuously tested under Linux, macOS, and Windows using the latest available system versions for GitHub Actions. MQT QMAP is compatible with all officially supported Python versions.

Cite This

Please cite the work that best fits your use case.

MQT QMAP (the tool)

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}
}

The Munich Quantum Toolkit (the project)

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}}
}

Peer-Reviewed Research

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.

Acknowledgements

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.

MQT Funding Footer

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mqt_qmap-3.8.0.tar.gz (1.6 MB view details)

Uploaded Jul 9, 2026 Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

mqt_qmap-3.8.0-cp314-cp314t-win_arm64.whl (8.2 MB view details)

Uploaded Jul 9, 2026 CPython 3.14tWindows ARM64

mqt_qmap-3.8.0-cp314-cp314t-win_amd64.whl (9.0 MB view details)

Uploaded Jul 9, 2026 CPython 3.14tWindows x86-64

mqt_qmap-3.8.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (14.6 MB view details)

Uploaded Jul 9, 2026 CPython 3.14tmanylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mqt_qmap-3.8.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (10.4 MB view details)

Uploaded Jul 9, 2026 CPython 3.14tmanylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mqt_qmap-3.8.0-cp314-cp314t-macosx_11_0_x86_64.whl (11.5 MB view details)

Uploaded Jul 9, 2026 CPython 3.14tmacOS 11.0+ x86-64

mqt_qmap-3.8.0-cp314-cp314t-macosx_11_0_arm64.whl (10.1 MB view details)

Uploaded Jul 9, 2026 CPython 3.14tmacOS 11.0+ ARM64

mqt_qmap-3.8.0-cp312-abi3-win_arm64.whl (8.0 MB view details)

Uploaded Jul 9, 2026 CPython 3.12+Windows ARM64

mqt_qmap-3.8.0-cp312-abi3-win_amd64.whl (8.7 MB view details)

Uploaded Jul 9, 2026 CPython 3.12+Windows x86-64

mqt_qmap-3.8.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (14.6 MB view details)

Uploaded Jul 9, 2026 CPython 3.12+manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mqt_qmap-3.8.0-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (10.4 MB view details)

Uploaded Jul 9, 2026 CPython 3.12+manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mqt_qmap-3.8.0-cp312-abi3-macosx_11_0_x86_64.whl (11.5 MB view details)

Uploaded Jul 9, 2026 CPython 3.12+macOS 11.0+ x86-64

mqt_qmap-3.8.0-cp312-abi3-macosx_11_0_arm64.whl (10.1 MB view details)

Uploaded Jul 9, 2026 CPython 3.12+macOS 11.0+ ARM64

mqt_qmap-3.8.0-cp311-cp311-win_arm64.whl (8.0 MB view details)

Uploaded Jul 9, 2026 CPython 3.11Windows ARM64

mqt_qmap-3.8.0-cp311-cp311-win_amd64.whl (8.7 MB view details)

Uploaded Jul 9, 2026 CPython 3.11Windows x86-64

mqt_qmap-3.8.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (14.6 MB view details)

Uploaded Jul 9, 2026 CPython 3.11manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mqt_qmap-3.8.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (10.4 MB view details)

Uploaded Jul 9, 2026 CPython 3.11manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mqt_qmap-3.8.0-cp311-cp311-macosx_11_0_x86_64.whl (11.5 MB view details)

Uploaded Jul 9, 2026 CPython 3.11macOS 11.0+ x86-64

mqt_qmap-3.8.0-cp311-cp311-macosx_11_0_arm64.whl (10.1 MB view details)

Uploaded Jul 9, 2026 CPython 3.11macOS 11.0+ ARM64

mqt_qmap-3.8.0-cp310-cp310-win_arm64.whl (8.0 MB view details)

Uploaded Jul 9, 2026 CPython 3.10Windows ARM64

mqt_qmap-3.8.0-cp310-cp310-win_amd64.whl (8.7 MB view details)

Uploaded Jul 9, 2026 CPython 3.10Windows x86-64

mqt_qmap-3.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (14.6 MB view details)

Uploaded Jul 9, 2026 CPython 3.10manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mqt_qmap-3.8.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (10.4 MB view details)

Uploaded Jul 9, 2026 CPython 3.10manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mqt_qmap-3.8.0-cp310-cp310-macosx_11_0_x86_64.whl (11.5 MB view details)

Uploaded Jul 9, 2026 CPython 3.10macOS 11.0+ x86-64

mqt_qmap-3.8.0-cp310-cp310-macosx_11_0_arm64.whl (10.1 MB view details)

Uploaded Jul 9, 2026 CPython 3.10macOS 11.0+ ARM64

File details

Details for the file mqt_qmap-3.8.0.tar.gz.

File metadata

Download URL: mqt_qmap-3.8.0.tar.gz
Upload date: Jul 9, 2026
Size: 1.6 MB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for mqt_qmap-3.8.0.tar.gz
Algorithm	Hash digest
SHA256	`a7ed773d45ee8759c7dc118b306f6cd9cb8199b436eab0108e70c478f7f91881`
MD5	`4cc1ba81493b7321a87b296a45198a90`
BLAKE2b-256	`1717558cd690ccc70ee3a4b13ac463c532757b62fac6cd723feeacb9037b4f39`

Algorithm	Hash digest
SHA256	`a15871bf9fe5fbdb6ec5d3b05c129842befb8123a1f9886134d493d2d2f4396d`
MD5	`e173fe1023d9c3dfaaec07fe2a02906a`
BLAKE2b-256	`ca7ee0f370e1609405884ce3ca980eb11f16037a510fbade9440be2d5cdf6a20`

Algorithm	Hash digest
SHA256	`46678de36887a0b520ccd08f671d5083b9f28db5bc20f3e5d288e79359156024`
MD5	`429482a7918fe7f874d76e63414f657a`
BLAKE2b-256	`19a1a7516c578bc5990d755bc3fc22e893bb234b9fa5d8d7b9169eb975a9a46f`

Algorithm	Hash digest
SHA256	`8bb8ea8de45ca42932cebec755f028ee02c503cb30a1a8e24b1cec106f88304c`
MD5	`3823e7b0826580777630a753832ea33a`
BLAKE2b-256	`837b86fbfb5d59fe73aaaf6706bcb29e189c14f4f05b04769ffc26be04f22770`

Algorithm	Hash digest
SHA256	`4c174c535afbf9f4151b9a01198865f8bbe461580a814db4f4632ad09c488532`
MD5	`ad1527fb6c7c48934ae79753be703bfc`
BLAKE2b-256	`9e0d74761c29f9e79df6bb2ea9031f85153d02b67de454635ac590d64353e635`

Algorithm	Hash digest
SHA256	`776d606c8548c3b918388214ffcad66b8814304e70bfed4aaabf23b4ff001491`
MD5	`9e7646cbc66156883256890b1bf2e02b`
BLAKE2b-256	`40db558dc04fc814cf11355ab2f539bee76a35519ca5c241a8e07e5398e4445e`

Algorithm	Hash digest
SHA256	`d55bcd7ecd0f0ec8249ecf9ed7fcdabcb84a3fee4f391dfaf7eaa3ed65032d4f`
MD5	`3624976591943d93f9d579f55e73702e`
BLAKE2b-256	`94003811bbf63fddc175fac15010955e4dfb7f683a45358ef07d0e5c219e4304`

Algorithm	Hash digest
SHA256	`831fa4c1eee6734e6989661420a1be3e5a28401df82e7247e1c884bc6d3bae5e`
MD5	`c73f6e6c86d6f3cad8c681dde5b8e222`
BLAKE2b-256	`f7c14ad09475671fd6c5c39f3fd6bab30d4330fb6947e2be0197464b292ac64d`

Algorithm	Hash digest
SHA256	`2e9098841c1947984cf5044c9a9eadaa96bf45d20cfe237d5883569f5e06dbd5`
MD5	`4a1d8c0a2a63fbee602dea07d104dc0a`
BLAKE2b-256	`38c20c6af2a6b321690156c21515f251341ae58aaa50b3aad7f9c628fbb3c2c6`

Algorithm	Hash digest
SHA256	`51156da404d6756cfff3b8c561df212b2fdec157cfe20d06b8500b6cc10fb05f`
MD5	`e580929e3fe807c5d647dffdf21cdafc`
BLAKE2b-256	`c0b0ac2978c12148d2673d7fd9ff93b19020811a19fb326cc5693a6cd382d0cd`

Algorithm	Hash digest
SHA256	`e293a742e7b6d8a450930de7c052e47735773c4a56209c7f343a9849a6f2e105`
MD5	`61723abd7cd624de5ff49607adcd59ff`
BLAKE2b-256	`680f16f0dbc7085acab367ae07c39f02bbcc2f17fde68fda6c9345d388d5e5b2`

Algorithm	Hash digest
SHA256	`6157e8485b9d618c4d010a26ed4fbe72d87d27c0c079e2451c8452cf2ce55c66`
MD5	`3f91b30b415bcfb0f1682eaae768df9d`
BLAKE2b-256	`195179dc815311fa45162ee409f740711b40d4500a62ff837ad6d984eb6d3a88`

Algorithm	Hash digest
SHA256	`2849764d8be03c7b9449d343ec99fa2fbd7515d7a5fb92f7711ebd995219fe36`
MD5	`bf0811ba69c469e1050458b88fc0c026`
BLAKE2b-256	`ed5e59875f930556d80826b8592d75f54692193cbcce69c1cf00c271a652b662`

Algorithm	Hash digest
SHA256	`5645b4947ae03f794113ace6742862db68f4f4aa7faecfb55d138c76f8286820`
MD5	`732901b442906691d20b844c44bf8212`
BLAKE2b-256	`65fb3c79c3145ea727cd685e486e7c9c553282785e8200e379e7b9b59212380b`

Algorithm	Hash digest
SHA256	`7a08ad382a92557b7a38d862ec24a116634e5621a7d389fa334841208de55e2e`
MD5	`f9b99cea1b5b8087ea88319bdec5142e`
BLAKE2b-256	`218a118df39c162492494ee24182fd5d8b3327eddb29eb5d06ee96a02368e635`

Algorithm	Hash digest
SHA256	`438069d74b9a058e1f2d85eaee71deba34425113fcdbda6d0a2eddc0b4daa019`
MD5	`8622c86880f652cee34b3e25372ba5d3`
BLAKE2b-256	`83b1570cbd42ea54ac1a6922f42817efcefdab5bf847d5514c1a442346c69bf9`

Algorithm	Hash digest
SHA256	`daf01a044f49cefbfd9adb0a91a8dd22fb1f076669a66ee12ab979e6a260fdb1`
MD5	`5ed0789f375ecf2a7943d3be5d9883b0`
BLAKE2b-256	`7c6a24683dca1efdd5911a942d395c67a097597e0bf7439b7f1acb3a443523cc`

Algorithm	Hash digest
SHA256	`1f0a3cfbe9d7b85ad14fd37c074733d4764ca0a1168c2c19ad7761d2448b3d32`
MD5	`809b85ea7e6d4ee8efb34a99cbfeb2b5`
BLAKE2b-256	`90c07f447e1367b31201657832c22b64a58594c1ac8fd8b73c3f04697faaa81a`

Algorithm	Hash digest
SHA256	`8d19d5ee2a261ecbd8eb889ddd95173cfd14c9e159e220de685f54086e01b5e3`
MD5	`af2e6219ae43660171de5fffa6f47906`
BLAKE2b-256	`e442726889ded585fa7c2e83e09fb8f8e422955979b8db224a0dad98b519cb57`

Algorithm	Hash digest
SHA256	`190993dbb6e7488b3584b6d89d522c2575c639c1526251df5e9726b55dd089ea`
MD5	`b4d804892f4d7f4deabedda7be6ff591`
BLAKE2b-256	`71475239ac0058b9977a01125f5c1d920c3e61c923b68d4053a43da9c1e4b29e`

Algorithm	Hash digest
SHA256	`49ea187bd2d2689447e2f91434bb127e1bd685ffe490af605bc6dd917344228d`
MD5	`f8a519bc2606491bdfc366ebeadc57eb`
BLAKE2b-256	`762d536805aca4c2dae17c56fe6309b5331f9facbb96d88bc39600059d6ed647`

mqt.qmap 3.8.0

MQT QMAP - A tool for Quantum Circuit Compilation

Key Features

Contributors and Supporters

Getting Started

System Requirements and Building

Cite This

MQT QMAP (the tool)

The Munich Quantum Toolkit (the project)

Peer-Reviewed Research

Acknowledgements

Project links

Key dates

Owner

Credits

GitHub Statistics

License expression

Requires

Provides Extra

Tags

Classifiers

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

Algorithm	Hash digest
SHA256	`ed0d3d2b615f05db8092866ad7d15fe1d5bdeb78a696bfdb41620c3287b18dee`
MD5	`9d1fcc820cc946f72e54ff57d1013d22`
BLAKE2b-256	`086b2d213ec76c9dc35836129b29090c05d00f897cf68984badfad393a7ab54a`

Algorithm	Hash digest
SHA256	`66ff13119c19523ca6ff46c8fb807e37b65a78259b8127c3941e3f28f90c5870`
MD5	`ff47933988c002d7b4bd76febd4f6703`
BLAKE2b-256	`168ff7c55f5e9f20a5a88d6d162d3a8177660dbe878e8a75018df33e66f84ebf`

Algorithm	Hash digest
SHA256	`14cea20bdb4e23a5be0e3f4328595af2ec24841214040524577653fe6fb05302`
MD5	`d0bfdfd97e5f2accdc92285e43dedc8e`
BLAKE2b-256	`050ab3cdc4e8b2575f2511ef53418a0561b74442bb0226892e6bba0ec5659808`

Algorithm	Hash digest
SHA256	`34a9ceb170354654cdac50620000dca06eab8b5af2ff9789981e025fbb1fde22`
MD5	`cca1f653f2892976e73feb034af740e6`
BLAKE2b-256	`439ebf1a300451b60563be86a702a67b2558f49e99f6f837f21d6c35d009a575`