Machine Learning for Advancing Quantum Field Theory-Enabled Sensor Networks in Precision Disease Detection: A Review - Khanal, Pratik; Khanal, Amrit (Crimson Journal of Science and Technology, 2025)
Quantum field theory (QFT)-enabled sensor networks are revolutionizing precision disease detection by exploiting quantum–classical correlations to achieve sensitivity and specificity beyond classical limits. Their diagnostic power, however, depends critically on machine learning (ML) for denoising, fusion, and interpretation of high-dimensional, entangled, and multimodal data streams. This review integrates advances at the ML–QFT interface across four pillars: (i) sensing foundations—NV-diamond and optically pumped magnetometry, cavity optomechanics, and quantum plasmonic or photonic systems; (ii) learning methods—physics-informed preprocessing (e.g., VAEs, diffusion models, PINNs), representation learning for sensor arrays and time series (GNNs, transformers), and hybrid quantum–classical architectures; (iii) applications—ultrasensitive pathogen detection, cancer biomarker profiling, neurodegenerative disease monitoring, and epidemiological surveillance; and (iv) cross-cutting enablers—adaptive calibration, federated and transfer learning, and explainable AI for clinical assurance. A practical ML–QFT co-design framework is presented, mapping model classes to sensor physics and deployment settings (edge/on-sensor versus cloud). An evaluation checklist coupling metrological and ML metrics—limit of detection, SNR uplift, calibration stability, latency, robustness under data shift, and interpretability—is proposed for benchmarking. Literature evidence shows that physics-aligned representations and hybrid learners consistently enhance performance in low-SNR and data-scarce regimes, though challenges remain from decoherence, drift, and scalability. The review concludes with a roadmap toward open benchmarks, quantum-networked distributed sensing, multiscale modeling linking molecular and population data, and certifiable, explainable ML pipelines—positioning ML not just as post-hoc analytics but as a design logic for globally distributed, self-calibrating quantum diagnostic ecosystems.