Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations
This addresses the issue of unreliable ML outputs for users in image classification, but it appears incremental as it builds on existing quantum-inspired methods.
The paper tackled the problem of machine learning systems generating confusing outputs or deferring to humans by using quantum-tunnelling neural networks inspired by human brain processes to classify images and emulate human perception. The result was that the QT-NN model showed potential to replicate human-like decision-making and outperform traditional ML algorithms.
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.