<p>Accurate detection of RR intervals in ECG signals is essential for reliable cardiac health assessment and early arrhythmia monitoring. The classical methods, specifically the Pan–Tompkins algorithm, suffer from noise sensitivity, increased false-positive detections, and difficulty in identifying low-amplitude R-peaks, particularly in real-time and long-duration ECG analysis. In order to overcome the classical limitations and by utilizing the advantage of quantum-enhanced signal analysis, we propose an AI-inspired quantum hybrid model that integrates Quantum Principal Component Analysis and Grover’s Algorithm for the detection of RR intervals. The proposed model is based on mapping the pipelined stages of classical Pan-Tompkins through quantum processing stages as amplitude encoding of ECG segments, QPCA-based noise reduction and feature compression, and based on amplitude amplification, the Grover's search performs structured and efficient R-peak detection, followed by RR intervals computed classically at the last. Our experimental results on a Synthetic Dataset and the MIT-BIH Arrhythmia Database have been used to validate that the proposed model, and for the real dataset a model achieved an accuracy of 98.8%, precision of 99.3%, and F1 score of 97.0%, with an improvement in the accuracy by 3.3% and an improvement in precision by around 19%, with the false positive detection reduced by nearly 96%. The implementation is realized using Qiskit-based simulation, preserving quantum algorithmic structure under NISQ constraints, and demonstrating the effectiveness of hybrid quantum-inspired approaches for biomedical signal analysis.</p>