GPU-Accelerated Quantum Simulation of Stabilizer Circuits

• URL: http://arxiv.org/abs/2603.14641v1
• Date: Sun, 15 Mar 2026 22:34:36 GMT
• Title: GPU-Accelerated Quantum Simulation of Stabilizer Circuits
• Abstract summary: We introduce new parallel algorithms for efficiently simulating stabilizer (Clifford) circuits on GPUs.<n>We implement these techniques in QuaSARQ, a GPU-accelerated stabilizer simulator for large qubit counts and many-shot sampling.
• Score: 0.084963488352489
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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$\times$ 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.

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