Four Generations of Quantum Biomedical Sensors
This work addresses the challenge of clinical translation for quantum biomedical sensors by organizing the field and proposing a roadmap, though it is incremental as it builds on existing quantum sensing concepts.
The paper proposes a generational framework to categorize quantum biomedical sensors based on their use of quantum resources, identifying four generations from classical scaling to quantum-enhanced adaptive inference, and analyzes bottlenecks to guide the transition from measuring physical observables to extracting structured biological information.
Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroscopic ensembles. We propose a unifying generational framework to organize the evolving landscape of quantum biosensors based on their utilization of quantum resources. First-generation devices utilize discrete energy levels for signal transduction but follow classical scaling laws. Second-generation sensors exploit quantum coherence to reach the standard quantum limit, while third-generation architectures leverage entanglement and spin squeezing to approach Heisenberg-limited precision. We further define an emerging fourth generation characterized by the end-to-end integration of quantum sensing with quantum learning and variational circuits, enabling adaptive inference directly within the quantum domain. By analyzing critical parameters such as bandwidth matching and sensor-tissue proximity, we identify key technological bottlenecks and propose a roadmap for transitioning from measuring physical observables to extracting structured biological information with quantum-enhanced intelligence.