The field of quantum computing and high-performance computing is rapidly evolving, with a focus on improving the efficiency and reliability of quantum systems. Recent developments have centered around the creation of novel frameworks, models, and algorithms that enable more effective utilization of quantum resources. Notably, researchers have been exploring the application of quantum computing to various domains, including natural language processing, bioprocess monitoring, and optimization problems. Furthermore, there is a growing interest in developing hybrid quantum-classical programming environments that can seamlessly integrate quantum and classical computing. The development of new programming languages, models, and frameworks is also underway, aiming to provide more efficient, flexible, and scalable solutions for quantum computing. Some noteworthy papers in this regard include: FIDDLE, which introduces a novel learning framework for quantum fidelity enhancement, and Quantum Approximate Optimization Algorithm for MIMO with Quantized b-bit Beamforming, which explores the use of QAOA for optimizing quantized beamforming in MIMO systems. Additionally, QuanBench is a benchmark for evaluating large language models on quantum code generation, and M2QCode is a model-driven framework for generating multi-platform quantum programs.