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Quantum Engineering Year 2: Advanced Quantum Science Month 30Day 830

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Year 2·Month 30·Week 3

Day 830: Neural Network Decoders

Day 830 of 2,016~20 min read

Learning Objectives

•**Explain** why neural networks are suitable for syndrome decoding
•**Design** neural network architectures for surface code decoding
•**Implement** training pipelines using simulated syndrome data
•**Analyze** the trade-offs between network size, accuracy, and inference speed
•**Compare** neural decoder thresholds to MWPM and Union-Find
•**Evaluate** hardware considerations for neural decoder deployment

Today's Schedule (7 hours)

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1 Why Neural Network Decoders 2 Problem Formulation Classification Approach Regression Approach 3 Neural Network Architectures Fully Connected Networks FCN Convolutional Neural Networks CNN Graph Neural Networks GNN RecurrentTransformer Networks 4 Training Strategy Data Generation Loss Function Balancing Classes 5 Threshold and Performance 6 Inference Speed Hardware Acceleration 7 Practical Considerations Generalization Model Size vs Latency Quantization

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