Introduces bounded old-state modulation via tanh gate to stabilize self-modulating QFWPs, with evaluations showing reduced divergence and improved robustness on quantum dynamics and SMS tasks.
In: 2025 International Wireless Communications and Mobile Computing (IWCMC)
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Quantum machine learning models do not surpass classical baselines in prediction performance, policy stability, or training time, though they may help filter noise and control false positives.
citing papers explorer
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Stable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates
Introduces bounded old-state modulation via tanh gate to stabilize self-modulating QFWPs, with evaluations showing reduced divergence and improved robustness on quantum dynamics and SMS tasks.
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Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
Quantum machine learning models do not surpass classical baselines in prediction performance, policy stability, or training time, though they may help filter noise and control false positives.