AmorphiQ is developing a breakthrough approach to machine learning through a hybrid quantum - classical system designed to unlock faster, more powerful analysis of complex time-series data using superconducting qubits. Many industries - from financial services to energy and automated manufacturing rely on the ability to process fast-changing, high-volume data streams in real time. However, classical machine learning systems often struggle with latency, high computational costs, and the inability to capture complex relationships in time-series data. In sectors like high-frequency trading or energy grid management, delays of even a millisecond can lead to lost revenue or missed optimisation opportunities Our platform is a joint development effort by academics from leading UK universities with decades of experience in the quantum domain. AmorphiQ harnesses the power of superconducting quantum circuits to act as a 'reservoir' that transforms input data through complex, nonlinear quantum dynamics. Only the readout layer is trained, reducing computational overhead and training time. This hybrid system capitalises on the strengths of both quantum and classical computing, delivering powerful performance on today's near-term quantum hardware. Initial applications are focused on: * **Financial services**: improving pattern recognition in high-frequency trading and fraud detection. * **Energy**: enhancing real-time grid optimisation and predictive maintenance. * **Manufacturing**: enabling rapid anomaly detection in automated processes. By delivering lower latency, reduced energy usage, and more accurate predictions, AmorphiQ offers a scalable and commercially viable quantum solution for data-intensive sectors.