Dispersive qubit readout with machine learning
- URL: http://arxiv.org/abs/2112.05332v1
- Date: Fri, 10 Dec 2021 04:25:43 GMT
- Title: Dispersive qubit readout with machine learning
- Authors: Enrico Rinaldi, Roberto Di Candia, Simone Felicetti, Fabrizio Minganti
- Abstract summary: Open quantum systems can undergo dissipative phase transitions, and their critical behavior can be exploited in sensing applications.
A recently introduced measurement protocol uses the parametric (two-photon driven) Kerr resonator's driven-dissipative phase transition to reach single-qubit detection fidelity of 99.9%.
We use machine learning-based classification algorithms to extract information from this critical dynamics.
- Score: 0.08399688944263842
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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