Neural-network-based qubit-environment characterization

Papic, Miha und Vega, Ines de (2022): Neural-network-based qubit-environment characterization. In: Physical Review A, Bd. 105, Nr. 2, 22605

Abstract

The exact microscopic structure of the environments that produce 1/f noise in superconducting qubits remains largely unknown, hindering our ability to create robust simulations and harness the noise. In this paper we show how it is possible to infer information about such an environment based on a single measurement of the qubit coherence, circumventing any need for separate spectroscopy experiments. Similarly to other spectroscopic techniques, the qubit is used as a probe which interacts with its environment. The complexity of the relationship between the observed qubit dynamics and the impurities in the environment makes this problem ideal for machine learning methods-more specifically neural networks. With our algorithm we are able to reconstruct the parameters of the most prominent impurities in the environment, as well as differentiate between different environment models, paving the way towards a better understanding of 1/f noise in superconducting circuits.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Physik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 530 Physik
• ISSN: 2469-9926
• Sprache: Englisch