AI 中文总结
该研究将CppSim张量网络模拟器扩展到重六边形晶格，用泡利转移矩阵模拟海森堡模型，纳入硬件噪声模型，在32GB GPU上实现χ收敛，相比参考实现有~10倍壁钟加速。
AI 中文摘要
我们将高性能C++/HIP张量网络模拟器CppSim扩展到超导量子处理器中使用的重六边形晶格几何结构。重六边形图作为一个即插即用的网格类实现，对门应用或置信传播（BP）内核无需更改。我们使用泡利转移矩阵（PTMs）在海森堡绘景中模拟重六边形3x3图（68个位点，76条键）上的各向同性海森堡模型，并研究自相关C(t) = <Zc (t)Zc (0)>和算符光锥。我们纳入了一个完全参数化的硬件噪声模型，在32GB GPU上，当χ = 200时实现了χ收敛（|误差| < 10^-4，参考χ = 430），在饱和键维度下，与Julia/AMDGPU.jl参考实现相比，壁钟速度提高了约10倍。
英文摘要
We extend CppSim, a high-performance C++/HIP tensor network simulator, to the heavy-hex lattice geometry used in superconducting quantum processors. The heavy-hex graph -- a bipartite graph of degree-2 and degree-3 sites with a natural 3-color gate schedule -- is implemented as a drop-in grid class with no changes to the gate application or belief propagation (BP) kernels. We simulate the isotropic Heisenberg model on the heavy hex 3x3 graph (68 sites, 76 bonds) in the Heisenberg picture using Pauli transfer matrices (PTMs), and study the autocorrelation C(t) = <Zc (t)Zc (0)> and operator lightcone. We incorporate a fully parametric hardware noise model: per-bond, per-color-class 16 x 16 PTMs are loaded from device characterization data and interleaved with unitary gates, with noise scaling factor gamma in {1.0, 2.0, 3.0} for multi-product formula (MPF) extrapolation to zero noise. Chi convergence is demonstrated at chi = 200 (|error| < 10^-4, reference chi = 430) on a 32 GB GPU, with a ~10x wall-clock speedup over a Julia/AMDGPU.jl reference implementation at saturated bond dimension.
Comments7 pages, 4 pictures, 1 table
