Hybrid Classical-Quantum Variational Autoencoder for Neural Topic Modeling

• URL: http://arxiv.org/abs/2606.13852v1
• Date: Thu, 11 Jun 2026 19:31:46 GMT
• Title: Hybrid Classical-Quantum Variational Autoencoder for Neural Topic Modeling
• Abstract summary: We present a proof-of-concept hybrid classical-quantum variational autoencoder (VAE) for topic modeling.<n>VAE embeds parameterized quantum circuits within the VAE inference network while retaining a classical topic-word decoder.<n>On the AgNews dataset, the hybrid VAE outperforms state-of-the-art neural topic models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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