Pauli Channel

Pauli channels, representing noise in quantum systems where errors are described by Pauli operators, are a central focus in quantum computing research. Current efforts concentrate on efficiently estimating the channel's properties, often employing algorithms based on Pauli-analytic techniques, quantum Chebyshev feature maps, or deep learning approaches, with a particular emphasis on minimizing the required quantum memory and measurements. These investigations are crucial for characterizing and mitigating noise in quantum devices, enabling more reliable quantum computation and paving the way for advancements in quantum information processing and related fields like quantum machine learning.