Wise, DF;
Morton, JJL;
Dhomkar, S;
(2021)
Using deep learning to understand and mitigate the qubit noise environment.
PRX Quantum
, 2
(1)
, Article 010316. 10.1103/PRXQuantum.2.010316.
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Abstract
Understanding the spectrum of noise acting on a qubit can yield valuable information about its environment, and crucially underpins the optimization of dynamical decoupling protocols that can mitigate such noise. However, extracting accurate noise spectra from typical time-dynamics measurements on qubits is intractable using standard methods. Here, we propose to address this challenge using deep learning algorithms, leveraging the remarkable progress made in the field of image recognition, natural language processing, and more recently, structured data. We demonstrate a neural network based methodology that allows for extraction of the noise spectrum associated with any qubit surrounded by an arbitrary bath, with significantly greater accuracy than the current methods of choice. The technique requires only a two-pulse echo decay curve as input data and can further be extended either for constructing customized optimal dynamical decoupling protocols or for obtaining critical qubit attributes such as its proximity to the sample surface. Our results can be applied to a wide range of qubit platforms, and provide a framework for improving qubit performance with applications not only in quantum computing and nanoscale sensing but also in material characterization techniques such as magnetic resonance.
Type: | Article |
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Title: | Using deep learning to understand and mitigate the qubit noise environment |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1103/PRXQuantum.2.010316 |
Publisher version: | http://dx.doi.org/10.1103/PRXQuantum.2.010316 |
Language: | English |
Additional information: | Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > London Centre for Nanotechnology |
URI: | https://discovery.ucl.ac.uk/id/eprint/10122012 |
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