Developed and delivered Quantum Optimization Adapters within the Jij-Inc/ommx repository, expanding support for additional quantum algorithms and enhancing the platform’s quantum optimization capabilities. Employed an adapter-based integration pattern in Python, allowing for seamless future algorithm additions with minimal code changes. Focused on maintainable, scalable architecture, the work consolidated new features into a single repository and aligned with established documentation and QA practices. No major bugs were reported during the release, reflecting careful implementation and review. Leveraged skills in quantum computing and data visualization to enable more flexible experimentation and reduce time-to-market for new quantum optimization features for end users.