一种能根据输入动态调整观测量的量子神经网络架构
Learning to Program Adaptive Non-Local Observables for Machine Learning
这是篇挺有意思的论文，作者提出了一种能根据输入动态调整观测量的量子神经网络架构，在时间序列预测和强化学习任务上表现不错。
这篇论文提出了一种名为QFWP-ANO的新型架构，它使用经典超网络来根据每个输入动态编程变分量子电路（VQC）的参数或非局域可观测量。在多变量时间序列预测任务上，该架构在四个ETT数据集的16个设置中实现了最低的均方误差（MSE），在另外四个设置中排名第二，超越了基于ANO的VQC和其他强基线。在强化学习任务中，QFWP-ANO也持续优于ANO-VQCs。
Learning to Program Adaptive Non-Local Observables for Machine Learning
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.