Machine learning for discriminating quantum measurement trajectories and improving readout

TL;DR

This paper applies advanced machine learning techniques to classify quantum measurement trajectories in superconducting qubits, significantly improving fidelity and diagnosing systematic errors to enhance quantum readout accuracy.

Contribution

It introduces the use of non-linear ML algorithms and clustering methods for quantum measurement classification, surpassing previous approaches and enabling error diagnosis.

Findings

Non-linear ML algorithms improve classification fidelity.

Clustering reveals systematic error sources.

Error diagnosis guides measurement improvements.

Abstract

High-fidelity measurements are important for the physical implementation of quantum information protocols. Current methods for classifying measurement trajectories in superconducting qubit systems produce fidelities that are systematically lower than those predicted by experimental parameters. Here, we place current classification methods within the framework of machine learning algorithms and improve on them by investigating more sophisticated ML approaches. We find that non-linear algorithms and clustering methods produce significantly higher assignment fidelities that help close the gap to the fidelity achievable under ideal noise conditions. Clustering methods group trajectories into natural subsets within the data, which allows for the diagnosis of specific systematic errors. We find large clusters in the data associated with relaxation processes and show these are the main source of discrepancy between our experimental and achievable fidelities. These error diagnosis techniques help provide a concrete path forward to improve qubit measurements.