Quantum Circuit for Imputation of Missing Data
The imputation of missing data is a common procedure in data analysis that consists in predicting missing values of incomplete data points. In this work, we analyze a variational quantum circuit for the imputation of missing data. We construct variational quantum circuits with gates complexity <i...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10643709/ |
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| author | Claudio Sanavio Simone Tibaldi Edoardo Tignone Elisa Ercolessi |
| author_facet | Claudio Sanavio Simone Tibaldi Edoardo Tignone Elisa Ercolessi |
| author_sort | Claudio Sanavio |
| collection | DOAJ |
| description | The imputation of missing data is a common procedure in data analysis that consists in predicting missing values of incomplete data points. In this work, we analyze a variational quantum circuit for the imputation of missing data. We construct variational quantum circuits with gates complexity <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N)$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N^{2})$</tex-math></inline-formula> that return the last missing bit of a binary string for a specific distribution. We train and test the performance of the algorithms on a series of datasets finding good convergence of the results. Finally, we test the circuit for generalization to unseen data. For simple systems, we are able to describe the circuit analytically, making it possible to skip the tedious and unresolved problem of training the circuit with repetitive measurements. We find beforehand the optimal values of the parameters and make use of them to construct an optimal circuit suited to the generation of truly random data. |
| format | Article |
| id | doaj-art-0947b252e5dd42b0a3791ac61ee67ad5 |
| institution | Kabale University |
| issn | 2689-1808 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-0947b252e5dd42b0a3791ac61ee67ad52025-01-28T00:02:29ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-01511210.1109/TQE.2024.344787510643709Quantum Circuit for Imputation of Missing DataClaudio Sanavio0https://orcid.org/0000-0002-5728-3289Simone Tibaldi1https://orcid.org/0000-0002-9257-5199Edoardo Tignone2https://orcid.org/0000-0002-8626-6744Elisa Ercolessi3https://orcid.org/0000-0002-6801-5976Fondazione Istituto Italiano di Tecnologia, Center for Life Nano-Neuroscience at la Sapienza, Roma, ItalyDipartimento di Fisica e Astronomia dell'Università di Bologna, Bologna, ItalyLeithá S.r.l., Unipol Group, Bologna, ItalyDipartimento di Fisica e Astronomia dell'Università di Bologna, Bologna, ItalyThe imputation of missing data is a common procedure in data analysis that consists in predicting missing values of incomplete data points. In this work, we analyze a variational quantum circuit for the imputation of missing data. We construct variational quantum circuits with gates complexity <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N)$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N^{2})$</tex-math></inline-formula> that return the last missing bit of a binary string for a specific distribution. We train and test the performance of the algorithms on a series of datasets finding good convergence of the results. Finally, we test the circuit for generalization to unseen data. For simple systems, we are able to describe the circuit analytically, making it possible to skip the tedious and unresolved problem of training the circuit with repetitive measurements. We find beforehand the optimal values of the parameters and make use of them to construct an optimal circuit suited to the generation of truly random data.https://ieeexplore.ieee.org/document/10643709/Imputation missing dataquantum computingvariational quantum circuit |
| spellingShingle | Claudio Sanavio Simone Tibaldi Edoardo Tignone Elisa Ercolessi Quantum Circuit for Imputation of Missing Data IEEE Transactions on Quantum Engineering Imputation missing data quantum computing variational quantum circuit |
| title | Quantum Circuit for Imputation of Missing Data |
| title_full | Quantum Circuit for Imputation of Missing Data |
| title_fullStr | Quantum Circuit for Imputation of Missing Data |
| title_full_unstemmed | Quantum Circuit for Imputation of Missing Data |
| title_short | Quantum Circuit for Imputation of Missing Data |
| title_sort | quantum circuit for imputation of missing data |
| topic | Imputation missing data quantum computing variational quantum circuit |
| url | https://ieeexplore.ieee.org/document/10643709/ |
| work_keys_str_mv | AT claudiosanavio quantumcircuitforimputationofmissingdata AT simonetibaldi quantumcircuitforimputationofmissingdata AT edoardotignone quantumcircuitforimputationofmissingdata AT elisaercolessi quantumcircuitforimputationofmissingdata |