Quantum computing is advancing rapidly, but even with future improvements in hardware and noise reduction, quantum devices alone will not be able to solve many large-scale real-world problems due to fundamental limitations in the number of logical qubits. To address this, it is essential to develop hybrid quantum-classical systems that combine quantum computing with high-performance computing (HPC). This project is focused on the application-aware co-design of hybrid algorithms to enable practical quantum advantage in optimization and machine learning tasks. These tasks are common in domains such as logistics, medical signal analysis, and materials science, all of which require real-time and large-scale processing. A large project is necessary to integrate expertise across quantum algorithms, high-performance computing systems, and domain-specific applications. This planning grant represents a critical step in building the collaborative infrastructure and technical foundation necessary for a successful large-scale NSF proposal in this emerging interdisciplinary area. This planning grant will support the conceptualization and design of new quantum-classical algorithmic pipelines specifically tailored for optimization and machine learning models. It will catalyze new collaborations among EPSCoR researchers and industry partners through joint research activities and a series of workshops. The team will evaluate the scalability and feasibility of variational quantum algorithms