Learning to Program Adaptive Non-Local Observables for Machine Learning

The paper introduces QFWP-ANO, a quantum neural network architecture that uses a classical hypernetwork to program variational quantum circuit parameters and non‑local observables conditioned on each input. Unlike existing adaptive non‑local observable (ANO) methods that learn a single static observable, QFWP-ANO dynamically adapts to each input. Experiments on multivariate time‑series forecasting and reinforcement learning tasks show that QFWP-ANO outperforms traditional ANO‑based VQCs and other strong baselines, achieving the lowest mean‑squared error in most settings.

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• reinforcement-learning