QASA with one 36-parameter quantum layer in a Transformer achieves best MSE on 4 of 9 synthetic tasks and 6% MAE reduction on ETTh1, outperforming larger quantum models on chaotic/noisy signals while classical models win on clean periodic data.
Qeegnet: Quantum machine learning for enhanced electroen- cephalography encoding
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Quantum Adaptive Self-Attention for Quantum Transformer Models
QASA with one 36-parameter quantum layer in a Transformer achieves best MSE on 4 of 9 synthetic tasks and 6% MAE reduction on ETTh1, outperforming larger quantum models on chaotic/noisy signals while classical models win on clean periodic data.