Quantum-Classical Data Fusion for Enhanced Sensor Design

Quantum sensors can surpass classical precision but are highly sensitive to environmental noise, creating analysis bottlenecks and obscuring target signals in high-dimensional measurements. The rapid advancement of ML and AI algorithms present a valuable way of handling complex and noisy data problems. We present a framework that treats sensing as an integrated quantum–classical data problem rather than a sensing problem, fusing unique classical and quantum datasets in novel ML pipelines. Primary measurements from quantum probes are combined with contextual streams from ancillary classical sensors not necessarily probing the same observable to achieve higher sensitivities, accuracies, and even detect latent features of the data. We then describe how multi-layer inversion pipeline (Bayesian and learned components) can disentangle signal from nuisance variability in real time, making these fusion processes viable for device implementation. We compare fusion architectures for thoughtfully combining various types of data and data structures. This approach mitigates failure modes of either sensing modality alone, enabling bias/drift control, uncertainty quantification, and recovery of otherwise hidden features. We illustrate the framework with simulation case studies and outline extensions to materials characterization and biomedical sensing. Our goal is to help in enabling robust, field-deployable, quantum-enhanced measurements grounded in reproducible fusion algorithms that will only become more valuable with advances in quantum device and AI technology.