The field of quantum computing and computer vision is rapidly advancing, with significant developments in areas such as quantum algorithms, tracking systems, and robotic manipulation. Researchers are exploring new techniques to improve the efficiency and accuracy of quantum computing, including the use of hybrid quantum-classical approaches and novel quantum algorithms. In computer vision, advancements in tracking systems, such as the use of dynamic graph fusion and temporal diffusion, are enabling more accurate and robust object tracking. Additionally, the integration of quantum computing and computer vision is leading to new applications in areas such as robotic perception and view planning. Notable papers in this area include: DARTer, which proposes a dynamic adaptive representation tracker for nighttime UAV tracking, achieving state-of-the-art results on multiple benchmarks. Qdislib, which introduces a distributed and flexible library for quantum circuit cutting, enabling the seamless integration with hybrid quantum-classical high-performance computing systems. Schrödingerization based quantum algorithms, which develop a quantum algorithm for solving high-dimensional fractional Poisson equations, demonstrating an exponential advantage over classical methods. GDSTrack, which proposes a novel approach for self-supervised RGB-T tracking, introducing dynamic graph fusion and temporal diffusion to address challenges in modality fusion and pseudo-label noise. HQC-NBV, which introduces a hybrid quantum-classical framework for view planning, achieving up to 49.2% higher exploration efficiency compared to classical methods.