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Quantum network control can leverage a compute/simulate/learn framework to dynamically estimate entanglement fidelity, with learned surrogates offering significant runtime speedups over simulators for online decision-making.
Pretrained object detectors hold untapped semantic knowledge that can be explicitly structured to dramatically improve out-of-distribution detection accuracy.