Gated QKAN fast-weight programmer achieves lowest pooled RMSE on Abilene TM forecasting while using 22.4% of a larger LSTM's parameters and outperforming classical G-FWP.
Quantum-enhanced channel mixing in rwkv models for time series forecasting,
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Self-Modulating QFWP adds adaptive modulation to quantum fast-weight updates and memory to improve stability and performance on sequential learning tasks.
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Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting
Gated QKAN fast-weight programmer achieves lowest pooled RMSE on Abilene TM forecasting while using 22.4% of a larger LSTM's parameters and outperforming classical G-FWP.
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Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning
Self-Modulating QFWP adds adaptive modulation to quantum fast-weight updates and memory to improve stability and performance on sequential learning tasks.