Nielsen and Chuang (2010) introduced three measures namely trace distance, fidelity, and VN-divergence.
the corresponding trainable components in state-of-art neural network architectures, namely, kernels in CNN and cells in RNN, are represented as arbitrary realvalued without any constraints, lead to difficulty to be understood.