面向量子电路裁剪后处理重构的CPU+DCU异构并行框架
A CPU+DCU Heterogeneous Parallel Framework for Post-Processing Reconstruction in Quantum Circuit Cutting
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中文总结 AI 辅助
针对量子电路裁剪后处理的计算与存储瓶颈，提出CPU+DCU异构并行框架，实现非零概率态重构，在嵩山超算上获最高259倍加速，可完成百量子比特规模任务。
中文摘要 AI 辅助
在含噪声中等规模量子（NISQ）时代，量子比特资源有限，难以直接在真实硬件上执行大规模量子电路。量子电路裁剪通过将大规模电路分解为多个小子电路缓解该限制，但会将大量开销转移到经典后处理中。随着电路规模、复杂度和裁剪数量增加，重构成为主要的计算和存储瓶颈。本文提出一种用于电路裁剪后处理重构的CPU+DCU异构并行框架，该框架未构建稠密的2ⁿ维概率向量，也未仅返回高概率态，而是从子电路测量结果中重构原始输出分布中的非零概率态。该框架结合了异构CPU+DCU执行、用于64位以上全局基态索引的高低字整数表示，以及跨越设备内存、主机内存和外存的三级协同存储机制。在嵩山超级计算机上的实验表明，该框架在保持高重构保真度的同时，针对线性簇态实现了比优化串行基准最高259倍的加速，针对随机电路实现了比同构CPU并行方法最高4倍的加速，还可完成百量子比特规模的重构任务。这些结果表明，面向高性能计算（HPC）的异构重构可有效缓解经典后处理瓶颈，提升重构可扩展性。
英文摘要
In the NISQ era, limited qubit resources make it difficult to execute large quantum circuits directly on real hardware. Quantum circuit cutting mitigates this limitation by decomposing a large circuit into smaller subcircuits, but it shifts substantial overhead to classical post-processing. As circuit size, complexity, and cut count increase, reconstruction becomes a major computational and storage bottleneck. This paper presents a CPU+DCU heterogeneous parallel framework for circuit-cutting post-processing reconstruction. Instead of constructing a dense $2^n$-dimensional probability vector or returning only high-probability states, the framework reconstructs the nonzero-probability states in the original output distribution from subcircuit measurement results. It combines heterogeneous CPU+DCU execution with a high/low-word integer representation for global basis-state indices beyond 64 bits and a three-level cooperative storage mechanism spanning device memory, host memory, and out-of-core storage. Experiments on the Songshan supercomputer show that the framework maintains high reconstruction fidelity while achieving up to $259\times$ speedup over an optimized serial baseline on linear-cluster states and up to $4\times$ speedup over a homogeneous CPU-parallel method on random circuits. The framework can also complete reconstruction tasks at the hundred-qubit scale. These results demonstrate that HPC-oriented heterogeneous reconstruction can effectively alleviate the classical post-processing bottleneck and improve reconstruction scalability.
