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 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.

Automated summary

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.