A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Article Swipe
Quanjun Lang
,
Jianfeng Lu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.14080
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.14080
Quantum superoperator learning is a pivotal task in quantum information science, enabling accurate reconstruction of unknown quantum operations from measurement data. We propose a robust approach based on the matrix sensing techniques for quantum superoperator learning that extends beyond the positive semidefinite case, encompassing both quantum channels and Lindbladians. We first introduce a randomized measurement design using a near-optimal number of measurements. By leveraging the restricted isometry property (RIP), we provide theoretical guarantees for the identifiability and recovery of low-rank superoperators in the presence of noise. Additionally, we propose a blockwise measurement design that restricts the tomography to the sub-blocks, significantly enhancing performance while maintaining a comparable scale of measurements. We also provide a performance guarantee for this setup. Our approach employs alternating least squares (ALS) with acceleration for optimization in matrix sensing. Numerical experiments validate the efficiency and scalability of the proposed methods.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2501.14080
- https://arxiv.org/pdf/2501.14080
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406840508
All OpenAlex metadata
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https://openalex.org/W4406840508Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2501.14080Digital Object Identifier
- Title
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A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix SensingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-23Full publication date if available
- Authors
-
Quanjun Lang, Jianfeng LuList of authors in order
- Landing page
-
https://arxiv.org/abs/2501.14080Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2501.14080Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2501.14080Direct OA link when available
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Matrix (chemical analysis), Rank (graph theory), Quantum, Computer science, Theoretical computer science, Mathematics, Physics, Quantum mechanics, Materials science, Combinatorics, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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