4.7 Article

Photonic band structure design using persistent homology

Journal

APL PHOTONICS
Volume 6, Issue 3, Pages -

Publisher

AMER INST PHYSICS
DOI: 10.1063/5.0041084

Keywords

-

Funding

  1. National Research Foundation, Prime Ministers Office, Singapore
  2. Ministry of Education, Singapore, under the Research Centres of Excellence program
  3. Polisimulator project
  4. EU Regional Development Fund

Ask authors/readers for more resources

Persistent homology is a machine learning technique that classifies complex systems or datasets by computing their topological features, showing promise for characterizing and optimizing band structures of periodic photonic media. It can reliably classify a variety of band structures falling outside the usual paradigms of topological band theory, including moat band and multi-valley dispersion relations, thereby controlling the properties of quantum emitters embedded in the lattice.
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 Moire superlattices.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available