Dispersive qubit readout with machine learning
No Thumbnail Available
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2021
Major/Subject
Mcode
Degree programme
Language
en
Pages
8
Series
Proceedings of the Machine Learning and the Physical Sciences: Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS)
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.Description
| openaire: EC/H2020/891517/EU//Green-MIQUEC
Keywords
Other note
Citation
Rinaldi, E, Di Candia, R, Felicetti, S & Minganti, F 2021, Dispersive qubit readout with machine learning . in Proceedings of the Machine Learning and the Physical Sciences : Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS) . Conference on Neural Information Processing Systems, Virtual, Online, 06/12/2021 . https://doi.org/10.48550/arXiv.2112.05332