# Conditional Quantum Flow Matching for Data-Scarce Physiological Signal Augmentation

arXiv: https://arxiv.org/abs/2609.14019 · HTML: https://mlsanity.com/paper/2609.14019/

## Key facts

- No citing papers in the map yet (data through 14 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.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 9 of 9)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Improving and generalizing flow-based generative models with minibatch optimal transport](https://mlsanity.com/paper/2302.00482/) (2023)
- [Revisiting Classifier Two-Sample Tests](https://mlsanity.com/paper/1610.06545/) (2016)
- [Generative quantum machine learning via denoising diffusion probabilistic models](https://mlsanity.com/paper/2310.05866/) (2023)
- [QEEGNet: Quantum Machine Learning for Enhanced Electroencephalography Encoding](https://mlsanity.com/paper/2407.19214/) (2024)
- [Quantum Multimodal Contrastive Learning Framework](https://mlsanity.com/paper/2408.13919/) (2024)
- [Quantum Flow Matching](https://mlsanity.com/paper/2508.12413/) (2025)
- [Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures](https://mlsanity.com/paper/2509.14163/) (2025)
- [Quantum Generative Diffusion Model for Real-World Time Series](https://mlsanity.com/paper/2606.27561/) (2026)

## 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).