With the complexity of ML models grow, traditional computing faces increased limitations, creating a new pathway for Quantum Computing to better address these challenges. In this session, we’ll explore how QNNs, hybrid quantum-classical AI, and quantum-inspired reinforcement learning could surpass classical models in solving practical challenges. As well as the hurdles of training AI on quantum hardware, the need for scalable infrastructure, and key breakthroughs needed to bridge the gap to quantum-driven intelligence.