Quantum process matrices as images: New tools to design novel denoising methods

Inferring a process matrix characterizing a quantum channel from experimental measurements is a key issue of quantum information. Noise affecting the measured counts could bring to matrices different from the expected ones and optimization methods usually employed, i.e. the maximum likelihood estimation (MLE), are characterized by several drawbacks. Lowering the noise could be necessary to increase the experimental resources, e.g. time for each measurement. In this paper, an alternative procedure, based on suitable Neural Networks, has been implemented and optimized to obtain a denoised process matrix and this approach has been tested with a specific quantum channel, i.e. a Control Phase. This promising method relies on the analogy that can be established between the elements of a process matrix and the pixels of an image.