Dispersive qubit readout with machine learning

E. Rinaldi, R. Di Candia, S. Felicetti, F. Minganti

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsProfessional

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 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.
Original languageEnglish
Title of host publicationProceedings of the Machine Learning and the Physical Sciences
Subtitle of host publicationWorkshop at the 35th Conference on Neural Information Processing Systems (NeurIPS)
Number of pages8
DOIs
Publication statusPublished - 2021
MoE publication typeD3 Professional conference proceedings
EventConference on Neural Information Processing Systems - Virtual, Online
Duration: 6 Dec 202114 Dec 2021
Conference number: 35
https://neurips.cc

Conference

ConferenceConference on Neural Information Processing Systems
Abbreviated titleNeurIPS
CityVirtual, Online
Period06/12/202114/12/2021
Internet address

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