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CompletedJanuary 15, 2026
Quantum Error Correction in Neural Networks
An investigation into applying quantum error correction algorithms to stabilize extremely large parameter neural networks during training.
Collaborating Partners
Stanford AI Lab
Academic Partner
NVIDIA
Hardware Compute Provider
This paper presents a novel approach to mitigating catastrophic forgetting and instability in trillion-parameter neural networks by drawing analogies to quantum error correction codes.
Key Findings
We demonstrated a 40% reduction in training variance across distributed GPU clusters when applying QEC-inspired regularizers.
Project Metadata
Project Status
published