发表机构
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center; Laboratory of Quantum Information, University of Science and Technology of China; Anhui Province Key Laboratory of Quantum Network, University of Science and Technology of China; Origin Quantum Computing Technology (Hefei) Co., Ltd.（合肥综合性国家科学中心人工智能研究院; 中国科学技术大学量子信息实验室; 中国科学技术大学安徽省量子网络重点实验室; 本源量子计算技术（合肥）有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于分支剪枝的经典模拟方法，利用无跳跃条件下的解析可预测性，仅演化坏分支，实现振幅阻尼桶链式QRAM的高效模拟，加速比达285倍。
AI 中文摘要
在噪声下对量子随机存取存储器（QRAM）进行经典模拟，可通过分支剪枝大幅加速：噪声历史被采样为轨迹系综，而好分支（其路由路径避开所有采样故障）无需演化，因为其最终状态是解析可预测的。在振幅阻尼下，这种可预测性并非自动成立，因为无跳跃算符 $K_0$ 在每个时间切片作用于每个分支。本文完整阐述了桶链式QRAM模拟中阻尼通道的可预测性，涵盖qutrit和qubit两种编码。对于qutrit编码，$K_0$ 在节点基下是对角的，等待态 $|W\rangle$ 是其不动点，因此好分支遵循无噪声轨道直至标量衰减。在qubit编码中，两个Hadamard壁之间的相位反冲存储器取回使 $K_0$ 不再对角，但所得结构可精确闭式求解。在无跳跃条件下，好分支的支内不保真度是阻尼率 $\gamma$ 的二阶量，每个数据qubit的相干泄漏约为 $n^2\gamma^2/4$，闭式解无近似地捕获该行为。我们还修正了qubit编码的剪枝准则。所得算法仅演化坏分支加上一个参考分支，并与它所替代的完整演化共享所有其他成本。通过端到端基准测试验证，剪枝相对完整演化的加速比高达 $285\times$，并通过轨迹级测试确认所有闭式解达到机器精度。
英文摘要
Classical simulation of quantum random access memory (QRAM) under noise can be accelerated dramatically by branch pruning: noise histories are sampled as trajectory ensembles, and good branches, whose routing paths avoid every sampled fault, need not be evolved because their final states are analytically predictable. Under amplitude damping this predictability is not automatic, because the no-jump operator $K_0$ acts on every branch at every time slice. Here we give a complete account of damping-channel predictability in bucket-brigade QRAM simulation, for both qutrit and qubit encodings. For the qutrit encoding, $K_0$ is diagonal in the node basis, and the wait state $|W\rangle$ is its fixed point, so a good branch follows the noiseless orbit up to a scalar attenuation. In the qubit encoding, phase-kickback memory fetching between two Hadamard walls makes $K_0$ no longer diagonal, yet the resulting structure is exactly solvable in closed form. Conditional on no jump, the in-branch infidelity of a good branch is second order in the damping rate $γ$, with coherent leakage $\sim n^2γ^2/4$ per data qubit, and the closed form captures it without approximation. We also revise the pruning criterion for the qubit encoding. The resulting algorithm evolves only the bad branches plus one reference branch and shares every other cost with the full evolution it replaces. It is validated by end-to-end benchmarks with pruned-over-full speedups up to $285\times$ and by trajectory-level tests confirming every closed form to machine precision.
Comments16 pages, 6 figures, 5 tables
