Blood Analysis using Quantum Sensor for different disease detection

The rapid advancements in technology and scientific research have led to an unprecedented demand for precise and reliable measurement systems. Traditional sensors, while highly effective in many domains, often face limitations in terms of sensitivity, accuracy, and environmental robustness. Unlike classical sensors, these devices offer unparalleled sensitivity and precision, enabling measurements of physical quantities such as magnetic fields, gravitational waves, and temperature with quantum-level accuracy. This study presents a comprehensive performance evaluation of various quantum sensing technologies for blood-based disease biomarker detection. Using simulation data from the Quantum Toolbox in Python (QuTiP), key performance metrics, including coherence time, signal-to-noise ratio, dynamic range, and entanglement, were analyzed across biomarkers. Results indicate that Quantum Coherence sensors exhibit superior diagnostic potential due to their high coherence and signal fidelity, followed by Atomic Magnetometers and Nitrogen vacancy Centers, which offer a balance between sensitivity and practical deployment. While Quantum Dots demonstrated strengths in resolution and entanglement, they were limited by noise susceptibility. Superconducting Quantum Interference Devices, despite their established reliability, showed reduced performance in critical quantum metrics. Bridging the gap between laboratory validation and clinical deployment will require interdisciplinary collaboration, standardization, and large-scale clinical trials.