The input consists of two bits that interact with the QRN for a period of time so that information about the input is fed into the QRN state. By reading QRN nodes and processing the result using a trained output layer, scientists can completely reconstruct the input state (tomography) or detect if the input quantum units are entangled. “Assuming that the sum of the training runs is the same in both conditions, we show that the swarm collective score estimates the input states better than the expert,” say the authors. This conclusion also applies to the entanglement detection task.
In order to be able to compare experts and groups, the same scientists dictated that the total number of training runs must be identical for both systems. However, if one relaxes this constraint, they write, even increasing swarm size offers the possibility of making the error rate arbitrarily low. This, in turn, is a prerequisite for being able to reliably use machine learning in real-world applications. “We hypothesize that our results can be generalized to many model quantum structures and quantum machine learning tasks.”
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