Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session Q71: Improving Coherence for Quantum Devices
3:00 PM–6:00 PM,
Wednesday, March 16, 2022
Room: Hyatt Regency Hotel -Jackson Park C
Sponsoring
Unit:
DMP
Chair: Yu Zhang, Los Alamos National Lab
Abstract: Q71.00011 : Large scale systematic characterization of CNTs for spin-qubit integration
5:48 PM–6:00 PM
Presenter:
Maria El Abbassi
(C12 quantum electronics)
Authors:
Maria El Abbassi
(C12 quantum electronics)
Frederik Van Veen
(Tu Delft)
Romaric Le Goff
(C12 quantum electronics)
Arthur Larrouy
(C12 quantum electronics)
Sergio De Bonis
(C12 quantum electronics)
Matthieu Delbecq
(ens paris)
Takis Kontos
(ENS Paris)
Joseph Sulpizio
(C12 quantum electronics)
Louis Virey
(c12 quantum electronics)
Davide Stefani
(c12 quantum electronics)
Matthieu Desjardins
(c12 quantum electronics)
Our approach relies on massive feedback between the micro-chip properties, the carbon nanotubes properties, and the measured qubit performance. The unique technology relies on the quality of the material (the CNT) and the assembling technology not involving any chemical\physical treatment. Furthermore, a large number of high quality microchips is needed to get this fast-feedback and tune workflow to make high quality qubits. A fast selection process of the ‘ideal’ CNTs is critical. In this talk, I will present the characterization tools we are developing to assess the quality of the tubes before and after integration. The pre-selection of CNTs is relevant because the tubes are assembled on the last fabrication step on a fully processed silicon chip that we believe helps to preserve its pristine properties. Before their integration, the carbon nanotubes are characterized and selected via optical spectroscopy (Raman and Rayleigh). From the optical spectra, we can extract information about the material itself (quality of the tube and contaminations) and its environment (metal contact and adsorbates). Those parameters influence extremely the qubit performance. We are also developing machine learning algorithms to analyse the large dataset that we will acquire to define an optimum fabrication recipe.
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