The recent developments in the research area indicate a significant shift towards hybrid quantum-classical algorithms and advanced numerical methods for solving complex problems. There is a notable emphasis on the application of these algorithms to specific classes of problems, such as tridiagonal systems of equations and finite element analyses, which are critical in various engineering and scientific domains. The integration of quantum computing with traditional computational methods is seen as a promising avenue for enhancing computational efficiency and accuracy. Additionally, there is a growing interest in Bayesian approaches for parameter identification and optimization, particularly in the context of structural health monitoring and digital twinning. These methods leverage statistical frameworks to improve predictions and decision-making processes. Furthermore, the field is witnessing advancements in the design and optimization of engineered systems, such as piezoelectric metastructures, which aim to enhance energy harvesting and vibration suppression. These developments underscore the interdisciplinary nature of the research, combining principles from quantum computing, statistics, and engineering to address real-world challenges. Notably, papers that present novel decompositions for quantum algorithms, efficient sampling-free statistical methods, and innovative designs for energy harvesting systems are particularly noteworthy for their contributions to advancing the field.