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.
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 language | English |
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Title of host publication | Proceedings of the Machine Learning and the Physical Sciences |
Subtitle of host publication | Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS) |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2021 |
MoE publication type | D3 Professional conference proceedings |
Event | Conference on Neural Information Processing Systems - Virtual, Online Duration: 6 Dec 2021 → 14 Dec 2021 Conference number: 35 https://neurips.cc |
Conference
Conference | Conference on Neural Information Processing Systems |
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Abbreviated title | NeurIPS |
City | Virtual, Online |
Period | 06/12/2021 → 14/12/2021 |
Internet address |