Experimental quantum learning of a spectral decomposition
Currently available quantum hardware allows for small-scale implementations of quantum machine learning algorithms. Such experiments aid the search for applications of quantum computers by benchmarking the near-term feasibility of candidate algorithms. Here we demonstrate the quantum learning of a t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2021-08-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.3.033200 |
| _version_ | 1827285641651552256 |
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| author | Michael R. Geller Zoë Holmes Patrick J. Coles Andrew Sornborger |
| author_facet | Michael R. Geller Zoë Holmes Patrick J. Coles Andrew Sornborger |
| author_sort | Michael R. Geller |
| collection | DOAJ |
| description | Currently available quantum hardware allows for small-scale implementations of quantum machine learning algorithms. Such experiments aid the search for applications of quantum computers by benchmarking the near-term feasibility of candidate algorithms. Here we demonstrate the quantum learning of a two-qubit unitary by a sequence of three parameterized quantum circuits containing a total of 21 variational parameters. Moreover, we variationally diagonalize the unitary to learn its spectral decomposition, i.e., its eigenvalues and eigenvectors. We illustrate how this can be used as a subroutine to compress the depth of dynamical quantum simulations. One can view our implementation as a demonstration of entanglement-enhanced machine learning, as only a single (entangled) training data pair is required to learn a 4×4 unitary matrix. |
| first_indexed | 2024-04-24T10:18:11Z |
| format | Article |
| id | doaj.art-34a61c76e8884b759da1c2d31ca1a183 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:18:11Z |
| publishDate | 2021-08-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-34a61c76e8884b759da1c2d31ca1a1832024-04-12T17:13:27ZengAmerican Physical SocietyPhysical Review Research2643-15642021-08-013303320010.1103/PhysRevResearch.3.033200Experimental quantum learning of a spectral decompositionMichael R. GellerZoë HolmesPatrick J. ColesAndrew SornborgerCurrently available quantum hardware allows for small-scale implementations of quantum machine learning algorithms. Such experiments aid the search for applications of quantum computers by benchmarking the near-term feasibility of candidate algorithms. Here we demonstrate the quantum learning of a two-qubit unitary by a sequence of three parameterized quantum circuits containing a total of 21 variational parameters. Moreover, we variationally diagonalize the unitary to learn its spectral decomposition, i.e., its eigenvalues and eigenvectors. We illustrate how this can be used as a subroutine to compress the depth of dynamical quantum simulations. One can view our implementation as a demonstration of entanglement-enhanced machine learning, as only a single (entangled) training data pair is required to learn a 4×4 unitary matrix.http://doi.org/10.1103/PhysRevResearch.3.033200 |
| spellingShingle | Michael R. Geller Zoë Holmes Patrick J. Coles Andrew Sornborger Experimental quantum learning of a spectral decomposition Physical Review Research |
| title | Experimental quantum learning of a spectral decomposition |
| title_full | Experimental quantum learning of a spectral decomposition |
| title_fullStr | Experimental quantum learning of a spectral decomposition |
| title_full_unstemmed | Experimental quantum learning of a spectral decomposition |
| title_short | Experimental quantum learning of a spectral decomposition |
| title_sort | experimental quantum learning of a spectral decomposition |
| url | http://doi.org/10.1103/PhysRevResearch.3.033200 |
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