Abstract: Artificial intelligence (AI) and quantum computing have become two highly influential fields in recent years. This study aims to investigate their bidirectional roles within the paradigm of “computation”: on one hand, examining the applications of AI in quantum research (AI for Quantum), and on the other, assessing the potential value of quantum computing for AI development (Quantum for AI). From the perspective of function approximation, this paper elaborates on the general value of AI in addressing scientific problems, and through comparative experiments on interpolation methods and theoretical model analysis, summarizes the core challenges it faces in terms of generalization, interpretability, and efficiency. The results indicate that AI can provide support at various stages of the quantum system lifecycle, yet its advantages remain constrained by limited generalization and interpretability. Meanwhile, in the context of Quantum for AI, complexity theory and error-correction cost estimation reveal that most current achievements lack sustainable industrial advantages, while “quantum-inspired” classical algorithms exhibit greater practical potential. In conclusion, although the integration of AI and quantum computing has demonstrated promising progress, long-term breakthroughs require further consolidation of theoretical foundations and methodological frameworks, particularly in strengthening interpretability and efficiency evaluation.