Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting
Sequence models must decide what to write into memory and what to retain. In quantum and quantum-inspired sequence learning, nonlinear recurrent updates often require repeated circuit evaluations and sequential backpropagation through time, making long contexts costly. Gated fast-weight programmers (FWPs) based on quantum-inspired Kolmogorov-Arnold networks (QKANs) alleviate this bottleneck by storing context in time-varying fast parameters. However, their scalar gate applies one retention-write balance to every fast-state coordinate, forcing all parameters to share a memory timescale. We introduce Self-Modulating QKAN-based FWPs, which replace this broadcast gate with low-rank-generated ...
| Authors | Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang et al. |
|---|---|
| Primary category | quant-ph |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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