mlsanityData to 14 Oct 2026

Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagnosis

arXiv 2609.01537 ↗First posted 1 Sep 2026cs.LGcited by 0

On the map: Mechanistic interpretability → Sparse autoencoder training and evaluation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Sparse autoencoder training and evaluation, one of 13 directions in Mechanistic interpretability. Builds on 6 of its 28 references that are in the map. First posted on arXiv 1 Sep 2026.

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6 of 28 references.

References · 6

  1. 2020Variational Quantum Algorithmscited by 692
  2. 2018Supervised learning with quantum enhanced feature spacescited by 665
  3. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  4. 2020Robust data encodings for quantum classifierscited by 91
  5. 2017The Sufficient and Necessary Condition for the Identifiability and Estimability of the DINA Modelcited by 2
  6. 2025Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decodingcited by 1

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