Simulating Clifford and near-Clifford circuits using the extended stabilizer formalism has become increasingly popular, particularly in quantum error correction. Compared to the state-vector approach, the extended stabilizer formalism can solve the same problems with fewer computational resources, as it operates on stabilizers rather than full state vectors. Most existing studies on near-Clifford circuits focus on balancing the trade-off between the number of ancilla qubits and simulation accuracy, often overlooking performance considerations. Furthermore, in the presence of high-rank stabilizers, performance is limited by the sequential property of the stabilizer formalism. In this work, we introduce a parallelized version of the extended stabilizer formalism, enabling efficient execution on multi-core devices such as GPU. Experimental results demonstrate that, in certain scenarios, our Python-based implementation outperforms state-of-the-art simulators such as Qiskit and Pennylane.

翻译：利用扩展稳定子形式模拟Clifford及近Clifford电路的方法日益受到关注，尤其在量子纠错领域。相较于态矢量方法，扩展稳定子形式通过操作稳定子而非完整态矢量，能够以更少的计算资源解决相同问题。现有关于近Clifford电路的研究多聚焦于辅助量子比特数量与模拟精度之间的权衡，常忽视性能考量。此外，当存在高秩稳定子时，稳定子形式的串行特性会限制性能表现。本研究提出并行化扩展稳定子形式，使其能在GPU等多核设备上高效运行。实验结果表明，在特定场景下，我们基于Python的实现方案在性能上超越了Qiskit和Pennylane等前沿模拟器。