Efficient self-consistent learning of gate set Pauli noise

Understand

Understanding quantum noise is an essential step towards building practical quantum information processing systems.

• Pauli noise is a useful model that has been widely applied in quantum benchmarking, error mitigation, and error correction.
• Despite intensive study, most existing works focus on learning Pauli noise channels associated with some specific gates rather than treating the gate set as a whole.
• A learning algorithm that is self-consistent, complete, and efficient at the same time is yet to be established.