Quantum Machine Learning Tensor Network States Article Swipe
Andrey Kardashin
,
Alexey Uvarov
,
Jacob Biamonte
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fphy.2020.586374
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fphy.2020.586374
Tensor network algorithms seek to minimize correlations to compress the classical data representing quantum states. Tensor network algorithms and similar tools—called tensor network methods—form the backbone of modern numerical methods used to simulate many-body physics and have a further range of applications in machine learning. Finding and contracting tensor network states is a computational task, which may be accelerated by quantum computing. We present a quantum algorithm that returns a classical description of a rank- r tensor network state satisfying an area law and approximating an eigenvector given black-box access to a unitary matrix. Our work creates a bridge between several contemporary approaches, including tensor networks, the variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and quantum computation.
Related Topics
Concepts
Tensor (intrinsic definition)
Quantum computer
Computer science
Tensor contraction
Eigenvalues and eigenvectors
Cartesian tensor
Unitary state
Quantum network
Quantum
Quantum state
Theoretical computer science
Mathematics
Tensor field
Tensor density
Physics
Quantum mechanics
Tensor product
Pure mathematics
Exact solutions in general relativity
Mathematical analysis
Law
Political science
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fphy.2020.586374
- https://www.frontiersin.org/articles/10.3389/fphy.2020.586374/pdf
- OA Status
- gold
- Cited By
- 21
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2971476734
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2971476734Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fphy.2020.586374Digital Object Identifier
- Title
-
Quantum Machine Learning Tensor Network StatesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-03-01Full publication date if available
- Authors
-
Andrey Kardashin, Alexey Uvarov, Jacob BiamonteList of authors in order
- Landing page
-
https://doi.org/10.3389/fphy.2020.586374Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fphy.2020.586374/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fphy.2020.586374/pdfDirect OA link when available
- Concepts
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Tensor (intrinsic definition), Quantum computer, Computer science, Tensor contraction, Eigenvalues and eigenvectors, Cartesian tensor, Unitary state, Quantum network, Quantum, Quantum state, Theoretical computer science, Mathematics, Tensor field, Tensor density, Physics, Quantum mechanics, Tensor product, Pure mathematics, Exact solutions in general relativity, Mathematical analysis, Law, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 6, 2022: 3, 2021: 7Per-year citation counts (last 5 years)
- References (count)
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48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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