Classifying single-qubit noise using machine learning

Understand

Quantum characterization, validation, and verification (QCVV) techniques are used to probe, characterize, diagnose, and detect errors in quantum information processors (QIPs).

• An important component of any QCVV protocol is a mapping from experimental data to an estimate of a property of a QIP.
• Machine learning (ML) algorithms can help automate the development of QCVV protocols, creating such maps by learning them from training data.
• We identify the critical components of "machine-learned" QCVV techniques, and present a rubric for developing them.