Learning to Program Adaptive Non-Local Observables for Machine Learning
cs.LG, quant-ph
Submitted: 2026-09-16
Updated: 2026-09-16
License: http://creativecommons.org/licenses/by/4.0/
The gist: Quantum neural networks (QNNs) are typically built from variational quantum circuits (VQCs), which are limited by local measurements.
Terminology
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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