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Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagnosis — arXiv:2609.01537

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.01537
License (as archived): CC0-1.0
Archived: 2026-10-17T06:52:25.972Z
SHA-256 of the archived text: 6a083c6d40d0ff539b6ff17089154138a0c742e4d5e6f4408f213234401175c3
Stable URL of this page: https://metraipolis.com/documento/dc_52fd65784bc2

How to cite

Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagnosis — arXiv:2609.01537 — arXiv (en), https://arxiv.org/abs/2609.01537 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_52fd65784bc2 (Metraipolis archive dc_52fd65784bc2, sha256 6a083c6d40d0ff539b6ff17089154138a0c742e4d5e6f4408f213234401175c3)

BibTeX

@misc{dc52fd65784bc2,
  title = {Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagnosis — arXiv:2609.01537},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.01537},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_52fd65784bc2; sha256 6a083c6d40d0ff539b6ff17089154138a0c742e4d5e6f4408f213234401175c3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.01537. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Q-matrices play a central role in cognitive diagnosis within educational data mining (EDM), specifying which latent skills each assessment item requires. Data-driven Q-matrix estimation remains challenging when assessments involve many correlated skills and when real response patterns depart from idealized generative assumptions. We introduce a novel quantum sparse autoencoder (QSAE) for Q-matrix estimation, which, to the best of our knowledge, is the first application of quantum machine learning (QML) to cognitive diagnosis. Overall, the QSAE embeds each student's binary response vector into a quantum circuit using an encoder, compresses it into a sparse latent representation, and maps that representation to the Q-matrix. We benchmark the QSAE against a classical autoencoder (CAE) across 60 simulated datasets and 9 real-world assessment datasets. The results reveal complementary strengths. Although the CAE partially achieves higher average accuracy under several simulation conditions, the QSAE is substantially more stable across replications, exhibiting lower variance in 49 of the 60 conditions. Moreover, on real assessment data, the QSAE outperforms the CAE on 6 of the 9 datasets. These findings suggest that the principal advancement of QML in this setting is not universal accuracy improvement, but enhanced robustness and capability to explore latent-structure complexity in real datasets.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-02 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.01537 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T06:41:55.865Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).