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.