Knapsack problems, a cornerstone of optimization, involve selecting the most valuable combination of items under a set of constraints. Classical computing struggles with these problems, especially as problem sizes grow larger. For knapsack problems, classical computers see an exponential increase in computational complexity with problem size. Quantum computing, with its parallel data processing capabilities, effectively addresses this scalability challenge allowing for larger and more complex problems to be solved. Classiq enables the use of quantum computing for these problems by automatically converting high-level problem descriptions into optimized quantum circuits. For instance, in optimizing financial portfolios, where item values and weights represent asset returns and risks, Classiq's platform allows users to easily model, synthesize, and execute quantum solutions, all in one platform, streamlining the entire process.