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GPU-Accelerated Quantum Simulation of Stabilizer Circuits

Muhammad Osama, Dimitrios Thanos, and Alfons Laarman

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AbstractWe introduce new parallel algorithms for efficiently simulating stabilizer (Clifford) circuits on GPUs, with a focus on data-parallel tableau evolution and scalable handling of projective measurements. Our approach reformulates key bottlenecks in stabilizer simulation – such as Gaussian elimination and measurement updates – into GPU-tailored primitives that eliminate sequential dependencies and maximize memory coalescing. We implement these techniques in QuaSARQ, a GPU-accelerated stabilizer simulator designed for large qubit counts and many-shot sampling. Across a broad benchmark suite reaching 180,000 qubits and depth 1,000 (roughly 130M gates), QuaSARQ shows substantial runtime improvements, with up to 105$\times$ speedup, and over 80% energy reduction on demanding instances. Moreover, QuaSARQ consistently outperforms Stim, a state-of-the-art CPU-optimized stabilizer simulator, as well as Qiskit-Aer (CPU/GPU), Qibo, Cirq, and PennyLane. Finally, QuaSARQ exhibits a significant advantage in many-shot sampling on large workloads. These results demonstrate that our parallel algorithms can significantly advance the scalability of stabilizer-circuit simulation, particularly for workloads involving extensive measurements and sampling.The open-source implementation of QuaSARQ is available at GitHub. Popular summaryQuantum computers need error correction before they can run useful algorithms reliably. Designing and operating error correction protocols requires fast classical methods for simulating large circuits consisting of so-called stabilizer or Clifford gates. Current simulation methods only reach tens of thousands of qubits, which is insufficient for modern error correction protocols. We studied whether GPUs, which execute thousands of threads at once, could do better. The obstacle is that the standard measurement algorithm is inherently sequential: each update depends on the result of the previous one, which is precisely the pattern a GPU cannot speed up. Our main insight was that this chain of updates can be rewritten as a prefix-XOR scan, an operation GPUs handle very well. Combined with a memory layout designed for the hardware, our simulator QuaSARQ handles circuits of up to 180,000 qubits and runs up to 105 times faster than existing simulators on the hardest instances, using over 80% less energy on demanding instances.► BibTeX data@article{Osama2026gpuaccelerated, doi = {10.22331/q-2026-10-01-2225}, url = {https://doi.org/10.22331/q-2026-10-01-2225}, title = {{GPU}-{A}ccelerated {Q}uantum {S}imulation of {S}tabilizer {C}ircuits}, author = {Osama, Muhammad and Thanos, Dimitrios and Laarman, Alfons}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {10}, pages = {2225}, month = oct, year = {2026} }► References [1] Dimitrios Thanos, Tim Coopmans, and Alfons Laarman. ``Fast Equivalence Checking of Quantum Circuits of Clifford Gates''. In Étienne André and Jun Sun, editors, Automated Technology for Verification and Analysis. Pages 199–216. Cham (2023).

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In Scott Lathrop, Jim Costa, and William Kramer, editors, Conference on High Performance Computing Networking, Storage and Analysis, SC 2011, Seattle, WA, USA, November 12-18, 2011. Pages 16:1–16:12. ACM (2011). doi: 10.1145/2063384.2063405. https://doi.org/10.1145/2063384.2063405 [40] Henri Bal, Dick Epema, Cees de Laat, Rob van Nieuwpoort, John Romein, Frank Seinstra, Cees Snoek, and Harry Wijshoff. ``A Medium-Scale Distributed System for Computer Science Research: Infrastructure for The Long Term''. IEEE Computer 49, 54–63 (2016). doi: 10.1109/MC.2016.127. https://doi.org/10.1109/MC.2016.127Cited byOn Crossref's cited-by service no data on citing works was found (last attempt 2026-10-11 10:01:09). Could not fetch ADS cited-by data during last attempt 2026-10-11 10:01:10: Cannot retrieve data from ADS due to rate limitations.This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions. AbstractWe introduce new parallel algorithms for efficiently simulating stabilizer (Clifford) circuits on GPUs, with a focus on data-parallel tableau evolution and scalable handling of projective measurements. Our approach reformulates key bottlenecks in stabilizer simulation – such as Gaussian elimination and measurement updates – into GPU-tailored primitives that eliminate sequential dependencies and maximize memory coalescing. We implement these techniques in QuaSARQ, a GPU-accelerated stabilizer simulator designed for large qubit counts and many-shot sampling. Across a broad benchmark suite reaching 180,000 qubits and depth 1,000 (roughly 130M gates), QuaSARQ shows substantial runtime improvements, with up to 105$\times$ speedup, and over 80% energy reduction on demanding instances. Moreover, QuaSARQ consistently outperforms Stim, a state-of-the-art CPU-optimized stabilizer simulator, as well as Qiskit-Aer (CPU/GPU), Qibo, Cirq, and PennyLane. Finally, QuaSARQ exhibits a significant advantage in many-shot sampling on large workloads. These results demonstrate that our parallel algorithms can significantly advance the scalability of stabilizer-circuit simulation, particularly for workloads involving extensive measurements and sampling.The open-source implementation of QuaSARQ is available at GitHub. Popular summaryQuantum computers need error correction before they can run useful algorithms reliably. Designing and operating error correction protocols requires fast classical methods for simulating large circuits consisting of so-called stabilizer or Clifford gates. Current simulation methods only reach tens of thousands of qubits, which is insufficient for modern error correction protocols. We studied whether GPUs, which execute thousands of threads at once, could do better. The obstacle is that the standard measurement algorithm is inherently sequential: each update depends on the result of the previous one, which is precisely the pattern a GPU cannot speed up. Our main insight was that this chain of updates can be rewritten as a prefix-XOR scan, an operation GPUs handle very well. Combined with a memory layout designed for the hardware, our simulator QuaSARQ handles circuits of up to 180,000 qubits and runs up to 105 times faster than existing simulators on the hardest instances, using over 80% less energy on demanding instances.► BibTeX data@article{Osama2026gpuaccelerated, doi = {10.22331/q-2026-10-01-2225}, url = {https://doi.org/10.22331/q-2026-10-01-2225}, title = {{GPU}-{A}ccelerated {Q}uantum {S}imulation of {S}tabilizer {C}ircuits}, author = {Osama, Muhammad and Thanos, Dimitrios and Laarman, Alfons}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {10}, pages = {2225}, month = oct, year = {2026} }► References [1] Dimitrios Thanos, Tim Coopmans, and Alfons Laarman. ``Fast Equivalence Checking of Quantum Circuits of Clifford Gates''. In Étienne André and Jun Sun, editors, Automated Technology for Verification and Analysis. Pages 199–216. Cham (2023).

In Thomas Neele and Anton Wijs, editors, Model Checking Software. Pages 106–134. Cham (2025).

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