The field of quantum computing is experiencing significant advancements, particularly in the areas of quantum error correction and hybrid quantum-classical computing. Recent developments in quantum error correction have focused on enhancing the scalability and efficiency of decoders for surface codes, with notable progress made in parallelizing fully neural network-based decoders. These advancements aim to meet the stringent latency requirements necessary for large-scale quantum platforms. Additionally, the integration of quantum computing with classical computing paradigms, through hybrid quantum-classical systems, is being rigorously explored to address the current limitations of quantum hardware. This approach seeks to leverage the strengths of both quantum and classical systems, thereby improving the reliability and performance of quantum applications. The empirical study of developer issues in hybrid quantum-classical applications highlights the practical challenges and provides actionable insights for future development. Overall, the field is moving towards more practical and scalable solutions that bridge the gap between theoretical promise and real-world application.