IBM掺杂克利福德采样实验的经典模拟与设计前沿
Classical Simulation and Design Frontiers for IBM's Doped Clifford Sampling Experiment
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中文总结 AI 辅助
本研究针对IBM掺杂克利福德采样实验，设计确定性时间边界张量网络收缩方法完成经典模拟，获精确振幅与log-XEB值，为实验诊断及未来实验设计提供工具。
中文摘要 AI 辅助
我们对IBM的掺杂克利福德随机电路采样实验进行了经典模拟，该实验包含70个量子比特、70个纠缠层以及468个插入的T门。我们专门设计了一种确定性时间边界张量网络收缩方法，以应对这类具有算子施密特秩为2的纠缠门的开边界一维砖式电路。对于深度为d的n量子比特电路，得到的未切片路径以收缩宽度⌈d/2⌉计算精确振幅；Ratcatcher计算证实，对于所测试的实例，不可能使用更小的宽度。由于单量子比特门被吸收时不会改变网络拓扑，收缩宽度以及密集调度的收缩成本与单量子比特门的取值、T门的数量及放置位置无关。对于IBM的实例，其最大中间张量包含2^35个complex64条目（比IBM的估计值小256倍），对应张量有效载荷为256 GiB，分布在一个节点内的8个GPU上。使用32个节点（每个节点配备8个NVIDIA H100 GPU），我们在37.3分钟内完成了对应IBM发布的输出比特串的全部2051个振幅批次。得到的概率产生的对数交叉熵基准（log-XEB）估计值为0.35034，95%置信区间为[0.29763, 0.40305]。在Porter-Thomas和 scrambled噪声假设下，该结果在数值上与IBM的保真度下界兼容；此外，保真度加权资源核算预测，在相同的32个节点上，583次收缩工作负载的完成时间为10.6分钟。更广泛而言，该方法为实验输出提供了实用诊断，并为未来掺杂克利福德采样实验的设计提供了定量工具。
英文摘要
We classically simulate the IBM doped Clifford random circuit sampling experiment, comprising $70$ qubits, $70$ entangling layers, and $468$ inserted $T$ gates. A deterministic temporal-boundary tensor network contraction approach is specifically designed to tackle such open-boundary one-dimensional brickwork circuits with operator-Schmidt-rank-$2$ entangling gates. For an $n$-qubit circuit of depth $d$, the resulting unsliced path evaluates an exact amplitude with contraction width $\lceil d/2\rceil$; Ratcatcher calculations certify that no smaller width is possible for the tested instances. Because one-qubit gates are absorbed without changing the network topology, the width and dense scheduled contraction cost are independent of their values and of the number and placement of $T$ gates. For the IBM instance, its largest intermediate tensor contains $2^{35}$ complex64 entries (256 times smaller than IBM's estimation), corresponding to a tensor payload of $256$ GiB, and is distributed across eight GPUs within a node. Using 32 nodes, with eight NVIDIA H100 GPUs per node, we completed all 2051 amplitude batches corresponding to IBM's published output bitstrings in 37.3 minutes. The resulting probabilities yield a log-XEB estimate of $0.35034$ with a 95\% interval of $[0.29763,0.40305]$. Under the Porter--Thomas and scrambled-noise assumptions, this is numerically compatible with IBM's fidelity lower bound; separately, fidelity-weighted resource accounting projects a 583-contraction workload with a 10.6-minute makespan on the same 32 nodes. More broadly, the approach provides a practical diagnostic for experimental outputs and a quantitative tool for designing future doped Clifford sampling experiments.
