Critical parametric quantum sensing with machine learning

Open quantum systems can undergo dissipative phase transitions, and their critical behavior can be used to enhance, e.g., the fidelity of superconducting qubit readout measurements, a central problem toward the creation of reliable quantum hardware. For example, 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\%. 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 fast and reliable measurement protocol in critical open quantum systems.