Dispersive qubit readout with machine learning

TL;DR

This paper enhances superconducting qubit readout fidelity by applying machine learning to analyze critical dynamics in a Kerr resonator, enabling faster and more reliable quantum measurements.

Contribution

It introduces machine learning algorithms to analyze time series data in critical quantum systems, improving qubit readout speed and accuracy.

Findings

Machine learning improves measurement speed.

Enhanced fidelity in qubit detection.

Effective analysis of critical quantum dynamics.

Abstract

Open quantum systems can undergo dissipative phase transitions, and their critical behavior can be exploited in sensing applications. For example, it can be used to enhance the fidelity of superconducting qubit readout measurements, a central problem toward the creation of reliable quantum hardware. A recently introduced measurement protocol, named ``critical parametric quantum sensing'', uses the parametric (two-photon driven) Kerr resonator's driven-dissipative phase transition to reach single-qubit detection fidelity of 99.9\% [arXiv:2107.04503]. In this work, we improve upon the previous protocol by using machine learning-based classification algorithms to \textit{efficiently and rapidly} extract information from this critical dynamics, which has so far been neglected to focus only on stationary properties. These classification algorithms are applied to the time series data of weak quantum measurements (homodyne detection) of a circuit-QED implementation of the Kerr resonator coupled to a superconducting qubit. This demonstrates how machine learning methods enable a faster and more reliable measurement protocol in critical open quantum systems.