This paper introduces an explainable AI framework comparing Quantum Boltzmann Machines (QBMs) and Classical Boltzmann Machines (CBMs) to enhance transparency in decision-making. By leveraging quantum principles for richer latent representations and focused feature attributions, the framework demonstrates improved accuracy and clearer identification of key influential features, advancing trustworthy AI systems.

This paper introduces the Spiking-Quantum Data Re-upload Convolutional Neural Network (SQDR-CNN), a novel architecture that enables joint training of spiking neural networks and quantum circuits. The model achieves competitive accuracy with significantly fewer parameters than state-of-the-art SNN baselines, showcasing the potential of hybrid neuromorphic and quantum systems for efficient machine learning.