QTAML derives WKB-based tunneling noise models for AI weights with affine mean drift and per-bit variance hierarchy, then uses them in TAC to achieve 95% clean accuracy with 3.4-33.6x less ECC overhead than baselines on CNNs and transformers.
arXiv preprint arXiv:2306.03076 , year=
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Quantum Tunneling-Aware Machine Learning: Physics-Derived Noise Models for Robust Deployment
QTAML derives WKB-based tunneling noise models for AI weights with affine mean drift and per-bit variance hierarchy, then uses them in TAC to achieve 95% clean accuracy with 3.4-33.6x less ECC overhead than baselines on CNNs and transformers.