Photonic band structure design using persistent homology
The machine learning technique of persistent homology classifies complex systems or datasets by computing their topological features over a range of characteristic scales. There is growing interest in applying persistent homology to characterize physical systems such as spin models and multiqubit en...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2021-03-01
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| Series: | APL Photonics |
| Online Access: | http://dx.doi.org/10.1063/5.0041084 |
| _version_ | 1818453512591245312 |
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| author | Daniel Leykam Dimitris G. Angelakis |
| author_facet | Daniel Leykam Dimitris G. Angelakis |
| author_sort | Daniel Leykam |
| collection | DOAJ |
| description | The machine learning technique of persistent homology classifies complex systems or datasets by computing their topological features over a range of characteristic scales. There is growing interest in applying persistent homology to characterize physical systems such as spin models and multiqubit entangled states. Here, we propose persistent homology as a tool for characterizing and optimizing band structures of periodic photonic media. Using the honeycomb photonic lattice Haldane model as an example, we show how persistent homology is able to reliably classify a variety of band structures falling outside the usual paradigms of topological band theory, including “moat band” and multi-valley dispersion relations, and thereby control the properties of quantum emitters embedded in the lattice. The method is promising for the automated design of more complex systems such as photonic crystals and Moiré superlattices. |
| first_indexed | 2024-12-14T21:40:10Z |
| format | Article |
| id | doaj.art-c20b866b2fbb463590fbd6ed8f37aad1 |
| institution | Directory Open Access Journal |
| issn | 2378-0967 |
| language | English |
| last_indexed | 2024-12-14T21:40:10Z |
| publishDate | 2021-03-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Photonics |
| spelling | doaj.art-c20b866b2fbb463590fbd6ed8f37aad12022-12-21T22:46:29ZengAIP Publishing LLCAPL Photonics2378-09672021-03-0163030802030802-910.1063/5.0041084Photonic band structure design using persistent homologyDaniel Leykam0Dimitris G. Angelakis1Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543The machine learning technique of persistent homology classifies complex systems or datasets by computing their topological features over a range of characteristic scales. There is growing interest in applying persistent homology to characterize physical systems such as spin models and multiqubit entangled states. Here, we propose persistent homology as a tool for characterizing and optimizing band structures of periodic photonic media. Using the honeycomb photonic lattice Haldane model as an example, we show how persistent homology is able to reliably classify a variety of band structures falling outside the usual paradigms of topological band theory, including “moat band” and multi-valley dispersion relations, and thereby control the properties of quantum emitters embedded in the lattice. The method is promising for the automated design of more complex systems such as photonic crystals and Moiré superlattices.http://dx.doi.org/10.1063/5.0041084 |
| spellingShingle | Daniel Leykam Dimitris G. Angelakis Photonic band structure design using persistent homology APL Photonics |
| title | Photonic band structure design using persistent homology |
| title_full | Photonic band structure design using persistent homology |
| title_fullStr | Photonic band structure design using persistent homology |
| title_full_unstemmed | Photonic band structure design using persistent homology |
| title_short | Photonic band structure design using persistent homology |
| title_sort | photonic band structure design using persistent homology |
| url | http://dx.doi.org/10.1063/5.0041084 |
| work_keys_str_mv | AT danielleykam photonicbandstructuredesignusingpersistenthomology AT dimitrisgangelakis photonicbandstructuredesignusingpersistenthomology |