mlsanityData to 7 Oct 2026

Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations

arXiv 2412.08010 ↗First posted 11 Dec 2024cs.LG physics.soc-ph quant-phcited by 4

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Abstract (arXiv)

Modern machine learning (ML) systems excel in recognising and classifying images with remarkable accuracy. However, like many computer software systems, they can fail by generating confusing or erroneous outputs or by deferring to human operators to interpret the results and make final decisions. In this paper, we employ the recently proposed quantum-tunnelling neural networks (QT-NNs), inspired by human brain processes, alongside quantum cognition theory, to classify image datasets while emulating human perception and judgment. Our findings suggest that the QT-NN model provides compelling evidence of its potential to replicate human-like decision-making and outperform traditional ML algorithms.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 33 of its 113 references that are in the map. First posted on arXiv 11 Dec 2024.

Builds on

33 of 113 references.

References · 33

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21 more
  1. 2020Classifying the classifier: dissecting the weight space of neural networkscited by 62
  2. 2019On a generalization of the Jensen-Shannon divergence and the JS-symmetrization of distances relying on abstract meanscited by 61
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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Full-Precision and Ternarised Neural Networks with Tunnel-Diode Activation Functions: Computing and Physics Perspectivescited by 3
  2. 2025Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applicationscited by 0
  3. 2025Resonant-Tunnelling Diode Reservoir Computing System for Image Recognitioncited by 0
  4. 2025Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysiscited by 0