MCMit mitigates mid-circuit measurement errors via a new multi-control branch instruction, CNN and transformer discriminators, and software techniques, reporting up to 70% latency reduction and 80% lower logical error rates in QEC.
End-to-end workflow for machine learning-based qubit readout with qick and hls4ml
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Physics-informed GNNs with four detector-aware graph constructions and a custom message passing layer achieve MAE 0.8525 for pT estimation on CMS trigger data with over 55% fewer parameters than baselines.
citing papers explorer
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MCMit: Mid-Circuit Measurement Error Mitigation
MCMit mitigates mid-circuit measurement errors via a new multi-control branch instruction, CNN and transformer discriminators, and software techniques, reporting up to 70% latency reduction and 80% lower logical error rates in QEC.
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Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
Physics-informed GNNs with four detector-aware graph constructions and a custom message passing layer achieve MAE 0.8525 for pT estimation on CMS trigger data with over 55% fewer parameters than baselines.