Fig. 4: Neural computation.
From: A co-design framework of neural networks and quantum circuits towards quantum advantage
a Prepossessing of inputs by (i) downsampling the original 28 × 28 image in MNIST to 4 × 4 image and (ii) get the 4 × 4 matrix and normalize gray level to [0, 1]. b In P-LYR, input data are converted from real number to a random variable following a two-point distribution. c Four operations in P-LYR, (i) R: converting a real number ranging from 0 to 1 to a random variable, (ii) C: average sum of weighted inputs, (iii) A: nonlinear activation function, (iv) E: converting random variable to a real number. d In U-LYR, m-input data are converted to a vector in the first column of a m × m unitary matrix. e Three operations in U-LYR, (i) U: unitary matrix converter, (ii) Cu: average sum of weighted inputs; (iii) Au: nonlinear activation function. f Normalization for the connection of P-LYR. g Normalization for the connection of U-LYR.
