云访问量子硬件上用于QAOA参数优化的批量模式搜索
Batched pattern search for QAOA parameter optimization on cloud-accessed quantum hardware
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中文总结 AI 辅助
本研究针对云量子处理器上QAOA参数优化，提出批量模式搜索方法，与COBYLA相比，在相同作业和射击次数下更快达到同等参数质量，并在投资组合选择实验中验证其有效性。
中文摘要 AI 辅助
在云访问的量子处理器上，变分算法的经典优化器通过提交的作业与设备通信，每个作业都带有排队和周转开销，该开销不依赖于作业中包含的电路数量。我们研究了这一点对量子近似优化算法（QAOA）的一个简单影响。如果一个优化器的下一组试验参数在任何结果返回之前就已确定，它可以在一个作业中评估整组参数，而像COBYLA这样的顺序优化器则每次函数评估花费一个作业。我们在IBM的\ibmfez\\处理器上，针对六到十二个资产的基数约束投资组合选择问题，将批量模式搜索与COBYLA进行了比较，给两个优化器相同的作业数量和相同的射击次数。在每个规模下，批量搜索在第一个作业后达到的参数质量，串行优化器需要多个作业才能匹配，并且两种方法收敛到相当的最终值；从四个起始点重复八资产比较，每次得到相同的排序。实例足够小，可以精确求解，这使我们能够检查设备的输出分布与投资组合的真实排名相关，并且与移除成本层的相同深度的控制电路明显分离。在经典最优参数下对电路深度进行简短扫描表明，在该设备上，测得的解质量在两层或三层QAOA时达到峰值。该协议描述得足够详细，可以复现，并且笔记本和原始计数已发布。
英文摘要
On a cloud-accessed quantum processor the classical optimizer of a variational algorithm communicates with the device through submitted jobs, and each job carries a queueing and turnaround overhead that does not depend on how many circuits it contains. We study a simple consequence of this for the quantum approximate optimization algorithm (QAOA). An optimizer whose next set of trial parameters is known before any result returns can evaluate the whole set in one job, whereas a sequential optimizer such as COBYLA spends one job per function evaluation. We compare a batched pattern search with COBYLA on IBM's \ibmfez\ processor for cardinality-constrained portfolio selection with six to twelve assets, giving both optimizers the same number of jobs and the same number of shots. At every size the batched search reaches, after its first job, a parameter quality that the serial optimizer takes several jobs to match, and the two methods converge to comparable final values; repeating the eight-asset comparison from four starting points gives the same ordering each time. The instances are small enough to be solved exactly, which lets us check that the device's output distribution is correlated with the true ranking of portfolios and is clearly separated from a control circuit of the same depth with the cost layer removed. A short scan over circuit depth at classically optimal parameters shows that on this device the measured solution quality peaks at two or three QAOA layers. The protocol is described in enough detail to be reproduced, and the notebooks and raw counts are released.
发表机构
- Lahore University of Management Sciences (LUMS)（拉合尔管理科学大学）
机构由 AI 辅助整理，请以论文原文为准。
