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This research introduces QFWP-ANO, a quantum neural network architecture that uses classical hypernetworks to dynamically adapt non-local observables based on specific inputs. By overcoming the limitations of static measurements in variational quantum circuits, the approach demonstrates superior performance in time-series forecasting and reinforcement learning tasks compared to existing baselines. The study marks a significant step toward enhancing the practical utility of quantum machine learning models through input-conditioned optimization strategies.
Learning to Program Adaptive Non-Local Observables for Machine Learning
arXiv:2609.18655v1 Announce Type: cross 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.
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.
Machine Learning (cs.LG); Quantum Physics (quant-ph)
arXiv:2609.18655 [cs.LG]
(or arXiv:2609.18655v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.18655 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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