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Machine Learning for Discriminating Quantum Measurement Trajectories and Improving Readout.

Easwar Magesan¹, Jay M Gambetta¹, A D Córcoles¹

¹IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA.

Physical Review Letters

|June 6, 2015

Summary

Machine learning improves superconducting qubit measurement classification. Advanced algorithms and clustering reveal T1 processes as a key error source, enhancing qubit fidelity.

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Area of Science:

Quantum computing
Superconducting circuits
Machine learning

Background:

Current superconducting qubit measurement classification methods yield lower fidelities than expected.
This discrepancy limits the achievable performance of quantum information processing.

Purpose of the Study:

To enhance superconducting qubit measurement classification using advanced machine learning algorithms.
To identify and diagnose systematic errors affecting qubit measurement fidelity.

Main Methods:

Classifying measurement trajectories using machine learning (ML) algorithms.
Investigating nonlinear algorithms and clustering methods for improved trajectory assignment.
Analyzing trajectory clusters to identify sources of experimental error.

Main Results:

Nonlinear ML algorithms and clustering methods significantly increase assignment fidelities.
Clustering reveals distinct trajectory groups, enabling systematic error diagnosis.
Large clusters linked to T1 processes are identified as the primary cause of fidelity loss.

Conclusions:

Advanced ML techniques, particularly clustering, can bridge the gap between experimental and ideal qubit fidelities.
Identifying T1 processes through error diagnosis offers a direct path to improving superconducting qubit measurements.
This work provides a framework for enhancing the reliability and performance of quantum computing systems.