Challenges we ran into:

1. Quantum-Classical Integration Complexity:
   Integrating the high-performance quantum computing backend with traditional classical AI algorithms and a user-friendly frontend proved initially complex. Managing data transfer, synchronization, and ensuring the seamless interaction between quantum processors and classical hardware required novel architectural design and robust error correction mechanisms. We addressed this by developing a specialized quantum-classical interface layer and optimizing data protocols for minimal latency.

2. Model Training and Data Handling at Scale:
   Training quantum-enhanced neural networks for medical imaging and genomics required handling extremely large and sensitive datasets, posing significant challenges in terms of computational resources, data privacy (HIPAA compliance), and model convergence. We overcame this by implementing distributed computing strategies, leveraging federated learning techniques where appropriate, and employing advanced data anonymization protocols.

3. Real-time Quantum Diagnostics Accuracy & Reliability:
   Ensuring the consistent accuracy and reliability of real-time quantum diagnostics (as in NexoGPT-1.2) across diverse patient data and clinical scenarios was a major hurdle. Initial inconsistencies in diagnostic outcomes, especially with edge cases, necessitated extensive validation. We debugged this by refining our quantum error correction codes, developing robust quality assurance pipelines, and conducting rigorous clinical simulations with diverse, large-scale medical datasets.