Learning Disentangled Representations with Quantum Variational Autoencoders
Variational autoencoders are powerful representation learning models that map complex data into low-dimensional latent spaces, enabling the discovery of interpretable and disentangled factors. Such representations can facilitate the interpretation and controllable generation of data describing complex scientific systems. Understanding how these factors are organized and encoded in latent space is therefore important for developing reliable representation learning models. Recently, quantum variational autoencoders (QVAEs) have been proposed as quantum representation models, demonstrating informative latent representations and improved latent-space occupancy through quantum regularization. ...
| Authors | Gaoyuan Wang, Jerry Tan, Mark Gerstein |
|---|---|
| Primary category | quant-ph |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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