条件量子流匹配用于数据稀缺的生理信号增强

中文摘要 AI 辅助

生成式增强是生理信号分类中标签稀缺的标准补救措施，但现有的量子生成模型从无信息噪声出发，忽略了已经可用的类别结构。我们提出条件量子流匹配（CQFM）：一个仅含306个参数的电路，同时以流时间和类别标签为条件，将紧凑的类条件先验分布输送到目标分布。已发表的量子流匹配是无条件的，因此据我们所知，这是首个条件版本，也是首个在参数化量子电路上进行脑电图（EEG）增强的工作。非负谱嵌入消除了读出时的层析成像需求。在BCI Competition IV-2a上，从先验而非噪声出发，相比QuDDPM（9/9受试者）在准确率上获得+5.1个百分点的提升，尽管在该工作点，类条件高斯分布与CQFM表现相当。当先验失效时，传输过程发挥作用：给定从其他受试者转移的先验，它重新获得+7.2个TSTR百分点（9/9）。

英文摘要

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).

发表机构

• Harvard Medical School（哈佛医学院）
• Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
• Brookhaven National Laboratory（布鲁克海文国家实验室）

机构由 AI 辅助整理，请以论文原文为准。