发表机构
Alexandru Ioan Cuza University; FreeYa Mind Campus（亚历山德鲁·伊万·库扎大学; FreeYa Mind园区）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出模块化基准框架，分离搜索方向与参数更新，评估多种优化器在QAOA、QCNN和VQE上的性能，揭示工作负载依赖的目标变化与评估成本。
AI 中文摘要
训练参数化量子电路需要在优化进展与有限的评估预算及近期量子硬件的统计不确定性之间取得平衡。我们提出了一个模块化基准测试框架，将量子搜索方向估计与经典参数更新规则分离，从而能够独立考察它们的相互作用和敏感性。我们使用量子近似优化算法（QAOA）评估了一组基于梯度、随机和无导数的优化器在组合优化中的表现，使用鸢尾花分类和用于二值MNIST分类的量子卷积神经网络（QCNN）评估了监督量子机器学习，并使用变分量子本征求解器（VQE）评估了分子氢的量子化学。通过在有噪声模拟和156量子比特处理器物理执行下，将目标评估与样本电路成本分离，我们比较了优化器在不同参数数量和测量需求的工作负载上的行为。四个选定的模拟器和硬件案例研究分别报告了最终和最佳观测目标，以及训练后的分类器准确率。比较包括选定的MaxCut和氢轨迹，并展示了依赖工作负载的目标变化和评估成本，而模拟器基准测试比较了三个种子下的峰值和平均性能。硬件运行未建立优化器排名，也未隔离设备噪声的因果效应。
英文摘要
Training parameterized quantum circuits requires balancing optimization progress with limited evaluation budgets and the statistical uncertainty of near-term quantum hardware. We present a modular benchmarking framework that separates quantum search-direction estimation from classical parameter-update rules, allowing their interactions and sensitivities to be examined independently. We evaluate a suite of gradient-based, stochastic, and derivative-free optimizers across combinatorial optimization using the Quantum Approximate Optimization Algorithm (QAOA), supervised quantum machine learning using Iris classification and a quantum convolutional neural network (QCNN) for binary MNIST classification, and quantum chemistry using the variational quantum eigensolver (VQE) for molecular hydrogen. By separating objective evaluations from sample-circuit costs under finite-shot simulation and physical execution on a 156-qubit processor, we compare optimizer behavior across workloads with different parameter counts and measurement requirements. Four selected simulator and hardware case studies report terminal and best observed objectives separately, alongside post-training classifier accuracies. The comparisons include selected MaxCut and hydrogen trajectories and illustrate workload-dependent objective changes and evaluation costs, while the simulator benchmark compares both peak and mean performance across three seeds. The hardware runs do not establish an optimizer ranking or isolate a causal effect of device noise.
