发表机构
Wells Fargo（富国银行）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出QFWP-ANO架构，利用经典超网络动态编程量子电路参数与非局域观测量，在时间序列预测和强化学习中优于现有方法。
AI 中文摘要
量子神经网络（QNNs）通常由变分量子电路（VQCs）构建，而这些电路受限于局域测量。自适应非局域观测量（ANO）通过联合优化电路参数和多量子比特测量来解决这一限制。然而，现有的基于ANO的VQCs仅学习一个单一的静态观测量，该观测量在所有输入上保持不变。我们提出QFWP-ANO，一种新颖的架构，它采用经典超网络来动态编程VQC参数和/或非局域观测量，使其以每个输入为条件。在四个ETT数据集上的多变量时间序列预测中，QFWP-ANO在20个设置中的16个中取得了最低的均方误差（MSE），在其余四个中取得第二低，超越了基于ANO的方法和其他强基线。在强化学习任务中，QFWP-ANO持续超越ANO-VQCs。我们的结果确立了输入条件化的ANO作为增强QNNs的有效方法。
英文摘要
Quantum neural networks (QNNs) are typically built from variational quantum circuits (VQCs), which are limited by local measurements. Adaptive non-local observables (ANO) address this by jointly optimizing circuit parameters and multi-qubit measurements. However, existing ANO-based VQCs learn only a single static observable that remains invariant across all inputs. We propose QFWP-ANO, a novel architecture which employs a classical hypernetwork to dynamically program VQC parameters and/or non-local observables conditioned on each input. On multivariate time-series forecasting across four ETT datasets, QFWP-ANO achieves the lowest MSE in 16 of 20 settings and second-lowest in the remaining four, surpassing ANO-based and other strong baselines. On reinforcement learning tasks, QFWP-ANO consistently surpasses ANO-VQCs. Our results establish input-conditioned ANO as an effective approach for enhancing QNNs.
