HQ-LP-FNO replaces part of the spectral channel mixing in a 3D FNO with a mode-shared VQC, reducing parameters by 15.6% and phase-fraction MAE by 26% on laser-processing surrogates while remaining stable under calibrated noise.
Amari, Neural Computation10, 251 (1998)
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A statistical mixture of Tanh and Swish activations with critical mixing fraction p_c induces a continuous phase transition to scale-invariant signal propagation in deep networks while preserving smoothness.
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Hybrid Fourier Neural Operator for Surrogate Modeling of Laser Processing with a Quantum-Circuit Mixer
HQ-LP-FNO replaces part of the spectral channel mixing in a 3D FNO with a mode-shared VQC, reducing parameters by 15.6% and phase-fraction MAE by 26% on laser-processing surrogates while remaining stable under calibrated noise.
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Competing nonlinearities, criticality, and order-to-chaos transition in deep networks
A statistical mixture of Tanh and Swish activations with critical mixing fraction p_c induces a continuous phase transition to scale-invariant signal propagation in deep networks while preserving smoothness.