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papersTODAY 04:00 UTC

Conditional Quantum Flow Matching Proposed for Physiological Signal Augmentation

Researchers propose a conditional quantum flow matching approach for generating synthetic physiological signals when labeled data is scarce. Unlike earlier quantum generative models that begin from uninformative noise, the method incorporates class structure already present in the data. The work targets label-scarce physiological signal classification tasks.

Conditional Quantum Flow Matchingdata augmentationlabel-scarcityphysiological-signal-classificationquantum-machine-learningsynthetic-data-generation

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