A transformer model with self-attention and auxiliary physics losses learns a direct non-iterative mapping from loads and fields to manufacturable optimized topologies.
Preskill, Quantum computing in the nisq era and beyond, Quantum 2 (2018) 79
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A temperature-scaled hybrid fusion of ResNet and trainable quantum circuit features reaches 87.82% accuracy on BreastMNIST, outperforming classical baselines.
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Physics-Informed Transformer for Real-Time High-Fidelity Topology Optimization
A transformer model with self-attention and auxiliary physics losses learns a direct non-iterative mapping from loads and fields to manufacturable optimized topologies.
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On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification
A temperature-scaled hybrid fusion of ResNet and trainable quantum circuit features reaches 87.82% accuracy on BreastMNIST, outperforming classical baselines.