Conference item : Poster
Improving dispersive readout of a superconducting qubit by machine learning on path signature
- Abstract:
- 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.
- Publication status:
- Accepted
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 285.1KB, Terms of use)
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- Publication website:
- https://neurips.cc/virtual/2023/76128
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- Grant:
- EP/N510129/1
- EP/S026347/1
- Acceptance date:
- 2023-10-28
- Event title:
- 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023): Workshop on Machine Learning and the Physical Sciences
- Event location:
- New Orleans, Louisiana, USA
- Event website:
- https://ml4physicalsciences.github.io/2023/
- Event start date:
- 2023-12-15
- Event end date:
- 2023-12-15
- Language:
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English
- Subtype:
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Poster
- Pubs id:
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2004486
- Local pid:
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pubs:2004486
- Deposit date:
-
2024-06-05
Terms of use
- Copyright holder:
- © 2023 The Author(s).
- Copyright date:
- 2023
- Notes:
- This paper was presented at the NeurIPS 2023 Workshop on Machine Learning and the Physical Sciences, 15th December 2023, New Orleans, USA. This is the accepted manuscript version of the paper. The final version is available online at https://neurips.cc/virtual/2023/76128 or https://ml4physicalsciences.github.io/2023/
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