This report highlights recent developments in quantum computing, optimization, and numerical methods, with a focus on innovative techniques and applications. The field of quantum computing is moving towards addressing the challenges of scalability, security, and practical application, with researchers exploring new methods for analyzing and optimizing quantum programs. Notable papers include Expectation-based Analysis of Higher-Order Quantum Programs and CloudQC: A Network-aware Framework for Multi-tenant Distributed Quantum Computing. The field of optimization is witnessing a significant shift towards quantum-enhanced techniques, with researchers exploring the application of quantum computing to address complex combinatorial optimization problems. Noteworthy papers in this area include EAQGA and Device-Algorithm Co-Design of Ferroelectric Compute-in-Memory In-Situ Annealer. The field of numerical methods is experiencing significant developments, with a focus on improving efficiency, accuracy, and scalability. Researchers are exploring new techniques for solving complex problems, such as tensor equations, singular kernel convolutions, and eigenvalue problems. Notable papers include Fast Singular-Kernel Convolution on General Non-Smooth Domains via Truncated Fourier Filtering and Breaking the Barrier of Self-Concordant Barriers: Faster Interior Point Methods for M-Matrices. Additionally, the fields of numerical analysis and acoustics, numerical methods for complex systems, and numerical methods for conservation laws and radiative transfer are also rapidly advancing, with a focus on developing more efficient and accurate methods for solving complex problems. Noteworthy papers in these areas include A paper presenting an efficient numerical algorithm for the inverse scattering problem of acoustic-elastic interaction with random periodic interfaces and A high-order energy-conserving semi-Lagrangian discontinuous Galerkin method for the Vlasov-Ampere system. The field of neural operators for partial differential equations is also rapidly advancing, with a focus on developing more accurate and efficient models for complex problems. Notable papers in this area include PODNO and DISCO. Furthermore, the field of machine learning is experiencing a significant shift with the integration of quantum computing, leading to innovative solutions in aerospace and molecular understanding. Notable papers include Capturing Aerodynamic Characteristics of ATTAS Aircraft with Evolving Intelligent System and Network Attack Traffic Detection With Hybrid Quantum-Enhanced Convolution Neural Network. Overall, these advancements have the potential to significantly impact a wide range of fields, from engineering and physics to machine learning and optimization.