Exploring Quantum-Assisted Denoising of FBG Sensor Data with Amplitude Encoding: A Perspective

Fiber Bragg grating (FBG) and surface plasmon resonance (SPR) sensors are integral to biomedical, structural, and environmental monitoring due to their high sensitivity and flexible design. These sensors operate based on the interaction of light (photons), making their behavior inherently governed by quantum mechanics. However, the measurements through these sensors often get degraded by noise introduced through environmental fluctuations and instrumentation imperfections. Consequently, robust and precise denoising techniques become essential for ensuring data integrity and extracting meaningful insights. The classical denoising methods such as Fourier Transfer based filtering face challenges in scalability and risk suppressing valuable signal features. By leveraging the quantum nature of both the sensing mechanism and the computational model, this perspective research explores a quantum-native approach using the Quantum Fourier Transform (QFT) in combination with amplitude encoding (AE) to denoise the FBG sensor data more effectively. The data strategy focuses on experimental transmittance signals collected from an Au-coated tilted FBG (i.e., Au-TFBG) sensor probe. The executions were conducted on IBM Quantum hardware across a range of qubit configurations. Our analysis indicates that AE within the 11–16 qubit range emerged to be offering high signal fidelity, robust noise suppression, and stable computational behavior. This study presents a fundamental perspective towards incorporating quantum computing into optical sensor signal processing. The proposed approach offers a scalable and physics-consistent solution for denoising. The findings support the potential of quantum computing to significantly enhance the precision, robustness, and real-time analytics capabilities of SPR and FBG sensor systems in diverse applications like healthcare diagnostics, defense, and environmental monitoring. A detailed outlook has been provided highlighting near- and far-future research directions.