一句话：提出树结构执行框架 TreeVQA，通过联合执行与按需分支复用任务间执行相似性，大幅减少变分量子算法的采样次数。
变分量子算法（VQA）在近期量子计算中很有前景，但执行代价高昂：每个任务需要多次迭代且每次迭代要运行大量线路，而真实应用往往包含多个任务，其数量随探索能量景观所需精度增长，导致所需采样次数（shots）极其庞大，实际执行代价难以承受。作者观察到同一应用中不同任务的量子执行存在可复用的相似性，据此提出 TreeVQA：先联合执行多个任务，仅当它们的量子执行发生分歧时才逐步分支，从而形成树状执行结构。TreeVQA 以 VQA wrapper 的形式实现，可与常见 VQA 应用集成。在科学与组合优化基准上的评估显示，在相同目标精度下采样次数平均减少 25.9 倍，大规模问题超过 100 倍，且收益随问题规模和精度要求提高而进一步增大。
Variational Quantum Algorithms (VQAs) are promising for near- and intermediate-term quantum computing, but their execution cost is substantial. Each task requires many iterations and numerous circuits per iteration, and real-world applications often involve multiple tasks, scaling with the precision needed to explore the application's energy landscape. This demands an enormous number of execution shots, making practical use prohibitively expensive. We observe that VQA costs can be significantly reduced by exploiting execution similarities across an application's tasks. Based on this insight, we propose TreeVQA, a tree-based execution framework that begins by executing tasks jointly and progressively branches only as their quantum executions diverge. Implemented as a VQA wrapper, TreeVQA integrates with typical VQA applications. Evaluations on scientific and combinatorial benchmarks show shot count reductions of 25.9× on average and over 100× for large-scale problems at the same target accuracy. The benefits grow further with increasing problem size and precision requirements.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2512-12068,
title = {TreeVQA: A Tree-Structured Execution Framework for Shot Reduction in Variational Quantum Algorithms},
author = {Yuewen Hou and Dhanvi Bharadwaj and Gokul Subramanian Ravi},
booktitle = {ASPLOS},
year = {2026},
doi = {10.1145/3779212.3790239}
}