Spectral thresholding quantum tomography for low rank states
The estimation of high dimensional quantum states is an important statistical problem arising in current quantum technology applications. A key example is the tomography of multiple ions states, employed in the validation of state preparation in ion trap experiments (Häffner et al 2005 Nature 438 64...
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| Format: | Article |
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IOP Publishing
2015
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| Online Access: | https://eprints.nottingham.ac.uk/47097/ |
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| author | Butucea, Cristina Guţă, Mădălin Kypraios, Theodore |
| author_facet | Butucea, Cristina Guţă, Mădălin Kypraios, Theodore |
| author_sort | Butucea, Cristina |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The estimation of high dimensional quantum states is an important statistical problem arising in current quantum technology applications. A key example is the tomography of multiple ions states, employed in the validation of state preparation in ion trap experiments (Häffner et al 2005 Nature 438 643). Since full tomography becomes unfeasible even for a small number of ions, there is a need to investigate lower dimensional statistical models which capture prior information about the state, and to devise estimation methods tailored to such models. In this paper we propose several new methods aimed at the efficient estimation of low rank states and analyse their performance for multiple ions tomography. All methods consist in first computing the least squares estimator, followed by its truncation to an appropriately chosen smaller rank. The latter is done by setting eigenvalues below a certain 'noise level' to zero, while keeping the rest unchanged, or normalizing them appropriately. We show that (up to logarithmic factors in the space dimension) the mean square error of the resulting estimators scales as where r is the rank, is the dimension of the Hilbert space, and N is the number of quantum samples. Furthermore we establish a lower bound for the asymptotic minimax risk which shows that the above scaling is optimal. The performance of the estimators is analysed in an extensive simulations study, with emphasis on the dependence on the state rank, and the number of measurement repetitions. We find that all estimators perform significantly better than the least squares, with the 'physical estimator' (which is a bona fide density matrix) slightly outperforming the other estimators. |
| first_indexed | 2025-11-14T20:04:20Z |
| format | Article |
| id | nottingham-47097 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:04:20Z |
| publishDate | 2015 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-470972020-05-04T17:22:19Z https://eprints.nottingham.ac.uk/47097/ Spectral thresholding quantum tomography for low rank states Butucea, Cristina Guţă, Mădălin Kypraios, Theodore The estimation of high dimensional quantum states is an important statistical problem arising in current quantum technology applications. A key example is the tomography of multiple ions states, employed in the validation of state preparation in ion trap experiments (Häffner et al 2005 Nature 438 643). Since full tomography becomes unfeasible even for a small number of ions, there is a need to investigate lower dimensional statistical models which capture prior information about the state, and to devise estimation methods tailored to such models. In this paper we propose several new methods aimed at the efficient estimation of low rank states and analyse their performance for multiple ions tomography. All methods consist in first computing the least squares estimator, followed by its truncation to an appropriately chosen smaller rank. The latter is done by setting eigenvalues below a certain 'noise level' to zero, while keeping the rest unchanged, or normalizing them appropriately. We show that (up to logarithmic factors in the space dimension) the mean square error of the resulting estimators scales as where r is the rank, is the dimension of the Hilbert space, and N is the number of quantum samples. Furthermore we establish a lower bound for the asymptotic minimax risk which shows that the above scaling is optimal. The performance of the estimators is analysed in an extensive simulations study, with emphasis on the dependence on the state rank, and the number of measurement repetitions. We find that all estimators perform significantly better than the least squares, with the 'physical estimator' (which is a bona fide density matrix) slightly outperforming the other estimators. IOP Publishing 2015-11-19 Article PeerReviewed Butucea, Cristina, Guţă, Mădălin and Kypraios, Theodore (2015) Spectral thresholding quantum tomography for low rank states. New Journal of Physics, 17 (11). 113050/1-113050/29. ISSN 1367-2630 quantum tomography low rank states thresholding estimation ion trap measurements rank penalization https://doi.org/10.1088/1367-2630/17/11/113050 doi:10.1088/1367-2630/17/11/113050 doi:10.1088/1367-2630/17/11/113050 |
| spellingShingle | quantum tomography low rank states thresholding estimation ion trap measurements rank penalization Butucea, Cristina Guţă, Mădălin Kypraios, Theodore Spectral thresholding quantum tomography for low rank states |
| title | Spectral thresholding quantum tomography for low rank states |
| title_full | Spectral thresholding quantum tomography for low rank states |
| title_fullStr | Spectral thresholding quantum tomography for low rank states |
| title_full_unstemmed | Spectral thresholding quantum tomography for low rank states |
| title_short | Spectral thresholding quantum tomography for low rank states |
| title_sort | spectral thresholding quantum tomography for low rank states |
| topic | quantum tomography low rank states thresholding estimation ion trap measurements rank penalization |
| url | https://eprints.nottingham.ac.uk/47097/ https://eprints.nottingham.ac.uk/47097/ https://eprints.nottingham.ac.uk/47097/ |