Abstract
In this paper, we present a hybrid quantum machine learning algorithm inspired by quantum reservoir computing (QRC). The objective of this study is to demonstrate improved prediction accuracy by developing a quantum-enhanced algorithm running on IBM's noise-free Qiskit simulator on classical hardware, and to compare its performance with classical machine learning techniques such as Ridge regression, XGBoost and CatBoost for medical insurance and health data analysis. While fault-tolerant quantum computers are not yet available, this approach offers a practical pathway for industries that require fewer qubits, higher accuracy, and are less sensitive to computation time in their data analysis workflows. Once quantum computers become fully operational and accessible for industrial-scale deployment, the proposed algorithm is expected to achieve similar results with significantly reduced computational time.