Conditional Quantum Flow Matching for Data-Scarce Physiological Signal Augmentation

2026-09-12

AI Summary

本文提出条件量子流匹配(CQFM)方法，利用类条件先验增强生理信号数据，解决了现有量子生成模型忽略类别结构的问题。

Abstract

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).

Problem

• Quantum Generative Models
• Label Scarcity
• Physiological Signal Classification
• Class Structure

Innovation

• Conditional Quantum Flow Matching
• Quantum Circuit
• Class-conditional Prior
• Nonnegative Spectral Embedding
• Data Augmentation