Quantum Reservoir Computing (QRC) is a near-term quantum machine learning paradigm for temporal data processing that requires no gradient-based optimization of the quantum system and is suited to noisy intermediate-scale quantum hardware. Participants will design and benchmark simulation-friendly QRC systems and apply them to real-world industry problems where chaotic dynamics and nonlinear temporal dependencies are central, including financial volatility prediction and climate and weather time-series forecasting. Teams will demonstrate performance across different qubit counts and realistic noise models, benchmark against classical baselines, and implement a common benchmark to validate that the quantum reservoir exhibits sufficient expressivity for forecasting complex, regime-shifting systems.