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arXiv · 2609.18655

Learning to Program Adaptive Non-Local Observables for Machine Learning

Yu-Ting Lee·Samuel Yen-Chi Chen·Huan-Hsin Tseng
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Abstract

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.

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Yu-Ting Lee, Samuel Yen-Chi Chen, Huan-Hsin Tseng. 2026-09-16. Learning to Program Adaptive Non-Local Observables for Machine Learning. https://arxiv.org/abs/2609.18655

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