Neural topic models enable scalable semantic discovery, but their integration with quantum hardware remains largely unexplored. We present a proof-of-concept hybrid classical-quantum variational autoencoder (VAE) for topic modeling, embedding parameterized quantum circuits within the VAE inference network while retaining a classical topic-word decoder. To address the resource constraints of quantum hardware, we propose a modified Gaussian Softmax posterior that decouples latent space dimensionality from the number of topics to be extracted, enabling the model to operate with a low-resource 10-qubit quantum device. On the AgNews dataset, the hybrid VAE outperforms state-of-the-art neural topic models (NTMs), reaching a $C_v$ coherence score of 0.71 and an NPMI score of 0.20 while preserving high topic diversity. For comparison, we also construct a fully classical variant, which also outperforms state-of-the-art models on AgNews and exhibits clear class separation in the latent space. These results demonstrate that hybrid VAEs are computationally viable even on NISQ-era devices and represent a promising direction for quantum-enhanced topic modeling.

翻译：神经主题模型能够实现可扩展的语义发现，但其与量子硬件的集成仍基本未经探索。我们提出了一种概念验证型混合经典-量子变分自编码器（VAE）用于主题建模，在VAE推理网络中嵌入参数化量子电路，同时保留经典主题-词解码器。为应对量子硬件的资源限制，我们提出一种改进的高斯Softmax后验分布，将潜空间维度与待提取主题数量解耦，使模型能够在低资源10量子比特量子设备上运行。在AgNews数据集上，该混合VAE优于现有最先进的神经主题模型（NTM），在保持高主题多样性的同时，达到了0.71的$C_v$连贯性分数和0.20的NPMI分数。作为对比，我们还构建了全经典变体，该变体在AgNews上也优于现有最先进模型，并在潜空间中展现出清晰的类别分离。这些结果表明，即使在NISQ时代设备上，混合VAE在计算上也是可行的，并且代表了量子增强主题建模的一个有前景的方向。