Artificial Neural Networks for Quantum Sensing: Metrologically Resourceful State Detection

The detection of fundamental quantum resources-namely coherence, discord, and entanglement-benchmarks the metrological power of quantum sensing networks. Traditional methods for certifying these resources, like exhaustive optimization-based tomographic procedures, are resource-intensive and vary significantly. This paper proposes a framework for identifying metrologically useful quantum sensing probes by detecting fundamental quantum resources. Herein, we introduce a witness-based certification method that is experimentally accessible and efficient, though it has limitations in reliability and universality. To address these, we employ artificial neural networks (ANNs) to classify quantum states into resourceful and resourceless states, enhancing the scope and reliability of proposed framework. The performance of ANN-based quantum state classification is also analyzed, positioning ANNs as effective tools for data-driven detection of metrologically resourceful states in quantum sensing tasks.