Improving dispersive readout of a superconducting qubit by machine learning on path signature
One major challenge that arises from quantum computing is to implement fast, high-accuracy quantum state readout. For superconducting circuits, this problem reduces to a time series classification problem on readout signals. We propose that using path signature methods to extract features can enhanc...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
2023
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| _version_ | 1826313133618102272 |
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| author | Cao, S Shao, Z Zheng, J-Q Bakr, M Leek, P Lyons, T |
| author_facet | Cao, S Shao, Z Zheng, J-Q Bakr, M Leek, P Lyons, T |
| author_sort | Cao, S |
| collection | OXFORD |
| description | One major challenge that arises from quantum computing is to implement fast, high-accuracy quantum state readout. For superconducting circuits, this problem reduces to a time series classification problem on readout signals. We propose that using path signature methods to extract features can enhance existing techniques for quantum state discrimination. We demonstrate the superior performance of our proposed approach over conventional methods in distinguishing three different quantum states on real experimental data from a superconducting transmon qubit. |
| first_indexed | 2024-09-25T04:08:16Z |
| format | Conference item |
| id | oxford-uuid:00e1072e-d946-417f-b2ce-4ff5657d2f4b |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:08:16Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | oxford-uuid:00e1072e-d946-417f-b2ce-4ff5657d2f4b2024-06-06T12:27:58ZImproving dispersive readout of a superconducting qubit by machine learning on path signatureConference itemhttp://purl.org/coar/resource_type/c_6670uuid:00e1072e-d946-417f-b2ce-4ff5657d2f4bEnglishSymplectic Elements2023Cao, SShao, ZZheng, J-QBakr, MLeek, PLyons, TOne major challenge that arises from quantum computing is to implement fast, high-accuracy quantum state readout. For superconducting circuits, this problem reduces to a time series classification problem on readout signals. We propose that using path signature methods to extract features can enhance existing techniques for quantum state discrimination. We demonstrate the superior performance of our proposed approach over conventional methods in distinguishing three different quantum states on real experimental data from a superconducting transmon qubit. |
| spellingShingle | Cao, S Shao, Z Zheng, J-Q Bakr, M Leek, P Lyons, T Improving dispersive readout of a superconducting qubit by machine learning on path signature |
| title | Improving dispersive readout of a superconducting qubit by machine learning on path signature |
| title_full | Improving dispersive readout of a superconducting qubit by machine learning on path signature |
| title_fullStr | Improving dispersive readout of a superconducting qubit by machine learning on path signature |
| title_full_unstemmed | Improving dispersive readout of a superconducting qubit by machine learning on path signature |
| title_short | Improving dispersive readout of a superconducting qubit by machine learning on path signature |
| title_sort | improving dispersive readout of a superconducting qubit by machine learning on path signature |
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