Leveraging machine learning features for linear optical interferometer control
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918039054712832 |
|---|---|
| author | Kuzmin, Sergei S. Dyakonov, Ivan V. Straupe, Stanislav S. |
| author_facet | Kuzmin, Sergei S. Dyakonov, Ivan V. Straupe, Stanislav S. |
| contents | We have developed an algorithm that constructs a model of a reconfigurable optical interferometer, independent of specific architectural constraints. The programming of unitary transformations on the interferometer's optical modes relies on either an analytical method for deriving the unitary matrix from a set of phase shifts or an optimization routine when such decomposition is not available. Our algorithm employs a supervised learning approach, aligning the interferometer model with a training set derived from the device being studied. A straightforward optimization procedure leverages this trained model to determine the phase shifts of the interferometer with a specific architecture, obtaining the required unitary transformation. This approach enables the effective tuning of interferometers without requiring a precise analytical solution, paving the way for the exploration of new interferometric circuit architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging machine learning features for linear optical interferometer control Kuzmin, Sergei S. Dyakonov, Ivan V. Straupe, Stanislav S. Quantum Physics Machine Learning Optics We have developed an algorithm that constructs a model of a reconfigurable optical interferometer, independent of specific architectural constraints. The programming of unitary transformations on the interferometer's optical modes relies on either an analytical method for deriving the unitary matrix from a set of phase shifts or an optimization routine when such decomposition is not available. Our algorithm employs a supervised learning approach, aligning the interferometer model with a training set derived from the device being studied. A straightforward optimization procedure leverages this trained model to determine the phase shifts of the interferometer with a specific architecture, obtaining the required unitary transformation. This approach enables the effective tuning of interferometers without requiring a precise analytical solution, paving the way for the exploration of new interferometric circuit architectures. |
| title | Leveraging machine learning features for linear optical interferometer control |
| topic | Quantum Physics Machine Learning Optics |
| url | https://arxiv.org/abs/2505.24032 |