Projects per year
Abstract
Quantum hypothesis testing plays a pivotal role in quantum technologies, making decisions or drawing conclusions about quantum systems based on observed data. Recently, quantum control techniques have been successfully applied to quantum hypothesis testing, enabling the reduction of error probabilities in the task of distinguishing magnetic fields in presence of environmental noise. In real-world physical systems, such control is prone to various channels of inaccuracies. Therefore improving the robustness of quantum control in the context of quantum hypothesis testing is crucial. In this work, we utilize optimal control methods to compare scenarios with and without accounting for the effects of signal frequency inaccuracies. For parallel dephasing and spontaneous emission, the optimal control inherently demonstrates a certain level of robustness, while in the case of transverse dephasing with an imperfect signal, it may result in a higher error probability compared to the uncontrolled scheme. To overcome these limitations, we introduce a robust control approach optimized for a range of signal noise, demonstrating superior robustness beyond the predefined tolerance window. On average, both the optimal control and robust control show improvements over the uncontrolled schemes for various dephasing or decay rates, with the robust control yielding the lowest error probability. © 2023 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft.
| Original language | English |
|---|---|
| Article number | 113026 |
| Journal | New Journal of Physics |
| Volume | 25 |
| Issue number | 11 |
| Online published | 17 Nov 2023 |
| DOIs | |
| Publication status | Published - Nov 2023 |
Research Keywords
- quantum hypothesis testing
- quantum control
- robust control
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
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Dive into the research topics of 'Quantum hypothesis testing via robust quantum control'. Together they form a unique fingerprint.Projects
- 3 Finished
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GRF: Quantum Control through Reinforcement Learning
WANG, X. S. (Principal Investigator / Project Coordinator)
1/01/21 → 12/06/25
Project: Research
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GRF: Robust Control of Quantum-dot Spin Qubits from Machine Learning
WANG, X. S. (Principal Investigator / Project Coordinator)
1/01/19 → 8/02/23
Project: Research
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GRF: Theory on Robust Manipulation of Silicon-based Spin Qubits
WANG, X. S. (Principal Investigator / Project Coordinator)
1/01/18 → 19/08/21
Project: Research