AP
A. Patwardhan
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Data-centric AI for QEC
Feature engineering for traditional ML models to solve QEC problem on real data
Quantum error correction (QEC) is one of the core challenges in building scalable quantum computers in the noisy intermediate-scale quantum (NISQ) era. Recently, AI-based QEC decoding has attracted significant interest across industry and academia, yet most efforts focus on large sequential deep learning models trained on simulated data. This leaves a gap in training machine learning decoders on purely real hardware data.
This work presents an empirical evaluation of a prototype data pipeline and hand-crafted feature registry for traditional machine learning-based QEC decoding on real data from QuTech and Google, contributing to a broader model lake vision. Ablation, accuracy decay analysis, and feature importance reveal that final-round and temporal defect features are most critical. Gradient boosting models remain competitive with sequential baselines for short sequences, and learned feature priorities shift with code distance.
Further comparison with LSTM-learned encodings on repetition code data reveals temporal interaction and mixing as the key direction for improving the temporal group in the hand-crafted feature registry. Together, these findings provide interpretable insight into real-data QEC decoding using simple models. ...
This work presents an empirical evaluation of a prototype data pipeline and hand-crafted feature registry for traditional machine learning-based QEC decoding on real data from QuTech and Google, contributing to a broader model lake vision. Ablation, accuracy decay analysis, and feature importance reveal that final-round and temporal defect features are most critical. Gradient boosting models remain competitive with sequential baselines for short sequences, and learned feature priorities shift with code distance.
Further comparison with LSTM-learned encodings on repetition code data reveals temporal interaction and mixing as the key direction for improving the temporal group in the hand-crafted feature registry. Together, these findings provide interpretable insight into real-data QEC decoding using simple models. ...
Quantum error correction (QEC) is one of the core challenges in building scalable quantum computers in the noisy intermediate-scale quantum (NISQ) era. Recently, AI-based QEC decoding has attracted significant interest across industry and academia, yet most efforts focus on large sequential deep learning models trained on simulated data. This leaves a gap in training machine learning decoders on purely real hardware data.
This work presents an empirical evaluation of a prototype data pipeline and hand-crafted feature registry for traditional machine learning-based QEC decoding on real data from QuTech and Google, contributing to a broader model lake vision. Ablation, accuracy decay analysis, and feature importance reveal that final-round and temporal defect features are most critical. Gradient boosting models remain competitive with sequential baselines for short sequences, and learned feature priorities shift with code distance.
Further comparison with LSTM-learned encodings on repetition code data reveals temporal interaction and mixing as the key direction for improving the temporal group in the hand-crafted feature registry. Together, these findings provide interpretable insight into real-data QEC decoding using simple models.
This work presents an empirical evaluation of a prototype data pipeline and hand-crafted feature registry for traditional machine learning-based QEC decoding on real data from QuTech and Google, contributing to a broader model lake vision. Ablation, accuracy decay analysis, and feature importance reveal that final-round and temporal defect features are most critical. Gradient boosting models remain competitive with sequential baselines for short sequences, and learned feature priorities shift with code distance.
Further comparison with LSTM-learned encodings on repetition code data reveals temporal interaction and mixing as the key direction for improving the temporal group in the hand-crafted feature registry. Together, these findings provide interpretable insight into real-data QEC decoding using simple models.