用量子认知神经网络模拟人类决策，提升模型可信度与准确性
Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations
- 结合量子隧穿机制与人类认知理论构建新模型
- 在图像分类中表现优于传统机器学习算法
- 适合需要可解释性与人机协同的场景
现代机器学习系统在图像识别与分类方面表现出色，但常产生模糊或错误输出，需依赖人工判断。本文采用受人类大脑机制启发的量子隧穿神经网络（QT-NN），融合量子认知理论，在图像数据集上进行分类，模拟人类感知与判断过程。结果表明，该模型在决策行为上展现出类人特征，其性能显著优于传统机器学习算法，为实现更具可信度与可解释性的智能系统提供了有力支持。
原文摘要 · Abstract (English)
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.
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