A permutation-equivariant deep learning model for quantum state characterization
The characterization of quantum states is a fundamental step of any application of quantum technologies. Nowadays, there exist several approaches addressing this problem, also based on machine and deep learning techniques. However, all these approaches usually require a number of measurements that s...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-06-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0258853 |
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| author | D. Maragnano C. Cusano M. Liscidini |
| author_facet | D. Maragnano C. Cusano M. Liscidini |
| author_sort | D. Maragnano |
| collection | DOAJ |
| description | The characterization of quantum states is a fundamental step of any application of quantum technologies. Nowadays, there exist several approaches addressing this problem, also based on machine and deep learning techniques. However, all these approaches usually require a number of measurements that scale exponentially with the number of parties composing the system. Threshold quantum state tomography (tQST) addresses this problem and, in some cases of interest, can significantly reduce the number of measurements. In this paper, we study how to combine a permutation-equivariant deep learning model with the tQST protocol. We test the model on quantum state tomography and purity estimation. Finally, we validate the robustness of the model to noise. We show results up to 4 qubits. |
| format | Article |
| id | doaj-art-d3939b70b64545ebb471c7996bedee86 |
| institution | Kabale University |
| issn | 2770-9019 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Machine Learning |
| spelling | doaj-art-d3939b70b64545ebb471c7996bedee862025-08-20T03:28:52ZengAIP Publishing LLCAPL Machine Learning2770-90192025-06-0132026110026110-1010.1063/5.0258853A permutation-equivariant deep learning model for quantum state characterizationD. Maragnano0C. Cusano1M. Liscidini2Dipartimento di Fisica, Università di Pavia, via Bassi 6, 27100 Pavia, ItalyDipartimento di Ingegneria Industriale e dell’Informazione, Università di Pavia, via Ferrata 5, 27100 Pavia, ItalyDipartimento di Fisica, Università di Pavia, via Bassi 6, 27100 Pavia, ItalyThe characterization of quantum states is a fundamental step of any application of quantum technologies. Nowadays, there exist several approaches addressing this problem, also based on machine and deep learning techniques. However, all these approaches usually require a number of measurements that scale exponentially with the number of parties composing the system. Threshold quantum state tomography (tQST) addresses this problem and, in some cases of interest, can significantly reduce the number of measurements. In this paper, we study how to combine a permutation-equivariant deep learning model with the tQST protocol. We test the model on quantum state tomography and purity estimation. Finally, we validate the robustness of the model to noise. We show results up to 4 qubits.http://dx.doi.org/10.1063/5.0258853 |
| spellingShingle | D. Maragnano C. Cusano M. Liscidini A permutation-equivariant deep learning model for quantum state characterization APL Machine Learning |
| title | A permutation-equivariant deep learning model for quantum state characterization |
| title_full | A permutation-equivariant deep learning model for quantum state characterization |
| title_fullStr | A permutation-equivariant deep learning model for quantum state characterization |
| title_full_unstemmed | A permutation-equivariant deep learning model for quantum state characterization |
| title_short | A permutation-equivariant deep learning model for quantum state characterization |
| title_sort | permutation equivariant deep learning model for quantum state characterization |
| url | http://dx.doi.org/10.1063/5.0258853 |
| work_keys_str_mv | AT dmaragnano apermutationequivariantdeeplearningmodelforquantumstatecharacterization AT ccusano apermutationequivariantdeeplearningmodelforquantumstatecharacterization AT mliscidini apermutationequivariantdeeplearningmodelforquantumstatecharacterization AT dmaragnano permutationequivariantdeeplearningmodelforquantumstatecharacterization AT ccusano permutationequivariantdeeplearningmodelforquantumstatecharacterization AT mliscidini permutationequivariantdeeplearningmodelforquantumstatecharacterization |