In the paper A quantum-implementable neural network model (Chen, Wang & Charbon, 2017), on page 18 they mention that "There are 784 qurons in the input layer, where each quron is comprised of ten qubits."
That seems like a misprint to me. After reading the first few pages I was under the impression that they were trying to use $10$ qubits to replicate the $784$ classical neurons in the input layer. Since $2^{10}=1024>784$, such that each sub-state's coefficient's square is proportional to the activity of a neuron. Say the square of the coefficient of $|0000000010\rangle$ could be proportional to the activation of the $2$-nd classical neuron (considering all the $784$ neurons were labelled fom $0$ to $783$).
But if what they wrote is true: "There are 784 qurons in the input layer" it would mean there are $7840$ qubits in the input layer, then I'm not sure how they managed to implement their model experimentally. As of now we can properly simulate only ~$50$ qubits.
However, they managed to give an error rate for $>7840$ qubits (see Page 21: "Proposed two-layer QPNN, ten hidden qurons, five select qurons - 2.38"). No idea how's they managed to get that value. Could someone please explain?