Gated QKAN-FWP combines fast weight programming with quantum-inspired Kolmogorov-Arnold networks via single-qubit DARUAN activations and gated updates to deliver a 12.5k-parameter model that outperforms larger classical RNNs on long-horizon solar forecasting while running on NISQ devices.
Timekan: Kan-based frequency decomposition learning architecture for long-term time series forecasting
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
AdaMamba adds input-dependent frequency bases and a unified time-frequency forgetting gate to Mamba, yielding higher forecasting accuracy than prior methods on standard long-term time series benchmarks.
citing papers explorer
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Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning
Gated QKAN-FWP combines fast weight programming with quantum-inspired Kolmogorov-Arnold networks via single-qubit DARUAN activations and gated updates to deliver a 12.5k-parameter model that outperforms larger classical RNNs on long-horizon solar forecasting while running on NISQ devices.
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AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting
AdaMamba adds input-dependent frequency bases and a unified time-frequency forgetting gate to Mamba, yielding higher forecasting accuracy than prior methods on standard long-term time series benchmarks.