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Quantum Machine Learning Quantum Simulation

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

arXiv

Authors: Milan Maksimovic, Ivan S. Maksymov

Year

2024

Paper ID

6134

Status

Preprint

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~2 min

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Abstract

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.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2024 reference point for readers tracking recent quantum research.
Modern machine learning (ML) systems excel in recognising and classifying images with remarkable accuracy.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2412.08010 [2] arXiv https://arxiv.org/abs/2412.08010 [3] Publisher https://arxiv.org/abs/2412.08010

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