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Peer-reviewedCC BY 4.0Quantum

GPU-Accelerated Quantum Simulation of Stabilizer Circuits

  • Muhammad Osama1
  • Dimitrios Thanos1
  • Alfons Laarman1
  1. 1Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands
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Abstract

We 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 × 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.

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Cite

BibTeXRIS
BibTeX
@article{osama2026gpuaccelerated,
  title = {{GPU-Accelerated Quantum Simulation of Stabilizer Circuits}},
  author = {Muhammad Osama and Dimitrios Thanos and Alfons Laarman},
  journal = {Quantum},
  year = {2026},
  month = oct,
  doi = {10.22331/q-2026-10-01-2225},
  url = {https://quantum-journal.org/papers/q-2026-10-01-2225/},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - GPU-Accelerated Quantum Simulation of Stabilizer Circuits
AU  - Muhammad Osama
AU  - Dimitrios Thanos
AU  - Alfons Laarman
PY  - 2026
DA  - 2026/10/01
JO  - Quantum
DO  - 10.22331/q-2026-10-01-2225
UR  - https://quantum-journal.org/papers/q-2026-10-01-2225/
AB  - We 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 × 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.
SN  - 2521-327X
ER  -