Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning
Recent advances in quantum machine learning have motivated efficient models for sequential data processing. In this paper, we propose Self-Modulating Quantum Fast Weight Programmers, or Self-Modulating QFWP, which extends Quantum Fast Weight Programmers by introducing adaptive modulation over both newly generated fast-weight updates and historical fast-weight memory. Numerical results show that the proposed mechanism improves convergence stability and prediction performance across varying model settings, including different numbers of qubits and input sequence lengths. We further provide theoretical arguments explaining how self-modulation balances new information injection with memory re...
| Authors | Samuel Yen-Chi Chen, Yifeng Peng, Kuo-Chung Peng et al. |
|---|---|
| Primary category | quant-ph |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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