Year: 2026
Type: preprint
Abstract: An orbital quantum sensor is more precise than classical and holds its information in a quantum state's superposition and entanglement, which classical measurement collapses. Learning on quantum-sensed data therefore requires a distributed system in which the data stay quantum from sensor to inference. We present EntangledEdge, the first system architecture to unify orbital quantum sensing and learning end-to-end, with a quantum data plane and a classical control plane. It routes each event to a channel-matched model in a capacity-diverse bank, adapts its detection threshold without labels, and aggregates across stations by hardware fidelity. Evaluated with a hybrid prototype (real quantum processors, simulated orbit-to-ground link) over satellite telemetry, EntangledEdge achieves high-quality inference at bounded per-event cost, improves quantum processor utilization, and scales with fleet size.
Source: arXiv (Cornell University)
Institutions: University of Utah, Rice University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214105620