SpooQ: Anatomy-Guided Quantum Skeleton Action Recognition

Quantum machine learning (QML) promises compact models that capture correlations costly to represent classically, yet has not been demonstrated on complex spatio-temporal problems such as skeleton-based action recognition. We introduce SpooQ, to our knowledge the first quantum machine learning architecture for skeleton-based action recognition, designed for near-term quantum hardware by encoding human anatomy directly into its circuit structure. On the NW-UCLA and a single-person subset of NTU RGB+D 60 cross-view benchmarks, SpooQ outperforms a parameter-matched state-of-the-art graph convolutional network by 7.0 and 3.5 accuracy points, reaching 84.3% and 68.5%, respectively. Under 1024-shot simulations using noise models derived from the IBM Brisbane and Prague backends, SpooQ retains approximately 80% accuracy and outperforms a flat quantum baseline by 27–38 accuracy points. These results extend QML beyond static benchmarks to a demanding spatio-temporal task and demonstrate robustness under finite-shot, backend-derived noise.