Stockholm-based startup FirstQFM unveiled a machine learning platform using patent-pending quantum foundation models (QFMs) to optimize Quantum Reservoir Computing (QRC) systems for enterprise forecasting, announced at the ISC High Performance 2026 conference in Germany. The platform generates localized, task-specific quantum feature layers, achieving a 56.1% series-level win rate in zero-shot predictive accuracy against leading classical time-series models. Quantum Reservoir Computing operates as a hybrid sequence-modeling framework where a low-depth quantum circuit serves as a high-dimensional feature generator, with FirstQFM using learned contextual information to tailor reservoirs to the physical state of the underlying processor and specific forecasting problems. The alpha-version system was evaluated on 41 daily financial return forecasting tasks spanning individual equities, global indices, crypto assets, and commodities, delivering a lower mean Mean Squared Error (0.000485 MSE) and higher directional accuracy than leading time-series foundation models developed by Google, Amazon, and Salesforce. Initial reservoirs were generated using the NVIDIA cuQuantum SDK and cuTensorNet libraries on the Leonardo Supercomputer, backed by the EuroHPC Joint Undertaking.