Optimized quantum sensor networks for ultralight dark matter detection

Dark matter (DM) remains one of the most compelling unresolved problems in fundamental physics, motivating the search for new detection approaches. We propose a network-based quantum sensor architecture to enhance sensitivity to ultralight DM fields. Each node in the network is a superconducting qubit, interconnected via controlled-Z gates in symmetric topologies such as line, ring, star, and fully connected graphs. We investigate four- and nine-qubit systems, optimizing both state preparation and measurement using a variational quantum metrology framework. This approach minimizes the quantum and classical Cramér-Rao bounds to identify optimal configurations. Bayesian inference is employed to extract the DM-induced phase shift from measurement outcomes. Our results show that optimized network configurations significantly outperform conventional Greenberger-Horne-Zeilinger-based protocols while maintaining shallow circuit depths compatible with noisy intermediate-scale quantum hardware. Sensitivity remains robust under local dephasing noise. These findings highlight the importance of network structure in quantum sensing and point toward scalable strategies for quantum-enhanced DM detection.