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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The authors introduce MuTA as a universal quantum neural network for MBQC and numerically demonstrate its ability to learn gates, classify quantum states, and process data under noise, including photonic hardware constraints.
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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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Measurement-based quantum machine learning
The authors introduce MuTA as a universal quantum neural network for MBQC and numerically demonstrate its ability to learn gates, classify quantum states, and process data under noise, including photonic hardware constraints.