mlsanityData to 7 Oct 2026

Conditional Quantum Flow Matching for Data-Scarce Physiological Signal Augmentation

arXiv 2609.14019 ↗First posted 12 Sep 2026quant-ph cs.LG cs.MMcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Generative augmentation is a standard remedy for label scarcity in physiological signal classification, but existing quantum generative models start from uninformative noise, ignoring class structure that is already available. We propose Conditional Quantum Flow Matching (CQFM): a single 306-parameter circuit, conditioned on both flow time and class label, transports a compact class-conditional prior toward the target distribution. Quantum flow matching as published is unconditional, so this is to our knowledge the first conditional one, and the first EEG augmentation on a parameterized quantum circuit. A nonnegative spectral embedding removes the need for tomography at readout. On BCI Competition IV-2a, starting from a prior rather than noise is worth +5.1 accuracy points over QuDDPM (9/9 subjects), though at that operating point a class-conditional Gaussian matches CQFM. Where the prior fails the transport earns its keep: given one transferred from other subjects it regains +7.2 TSTR points (9/9).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 9 of its 16 references that are in the map. First posted on arXiv 12 Sep 2026.

Builds on

9 of 16 references.

References · 9

  1. 2022Flow Matching for Generative Modelingcited by 5,426
  2. 2023Improving and generalizing flow-based generative models with minibatch optimal transportcited by 835
  3. 2016Revisiting Classifier Two-Sample Testscited by 258
  4. 2023Generative quantum machine learning via denoising diffusion probabilistic modelscited by 30
  5. 2024QEEGNet: Quantum Machine Learning for Enhanced Electroencephalography Encodingcited by 13
  6. 2024Quantum Multimodal Contrastive Learning Frameworkcited by 9
  7. 2025Quantum Flow Matchingcited by 4
  8. 2025Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architecturescited by 2
  9. 2026Quantum Generative Diffusion Model for Real-World Time Seriescited by 1

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  1. 2026Sensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals
  2. 2026B[FM]2: Brain Foundation Model via Flow Matching with SplitUNet
  3. 2026Conditional Flow Matching for Single-Neuron Electrophysiology: Capturing Multimodal Responses Across Stimuli
  4. 2026Conditional Flow Matching for Transport Between Markov Processes
  5. 2026NEUROTOKEN: Joint Source and Directional AAD with Envelope Decoding via Conditional Flow Matching
  6. 2026Quantum MeanFlow: single-shot generative sampling on NISQ hardware
  7. 2024Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
  8. 2026Let EEG Models Learn EEG