Neuromorphic Binarized Polariton Networks

The rapid development of artificial neural networks and applied artificial intelligence has led to many applications. However, current software implementation of neural networks is severely limited in terms of performance and energy efficiency. It is believed that further progress requires the development of neuromorphic systems, in which hardware directly mimics the neuronal network structure of a human brain. Here, we propose theoretically and realize experimentally an optical network of nodes performing binary operations. The nonlinearity required for efficient computation is provided by semiconductor microcavities in the strong quantum light-matter coupling regime, which exhibit exciton-polariton interactions. We demonstrate the system performance against a pattern recognition task, obtaining accuracy on a par with state-of-the-art hardware implementations. Our work opens the way to ultrafast and energy-efficient neuromorphic systems taking advantage of ultrastrong optical nonlinearity of polaritons.

Automated summary

The rapid development of artificial neural networks and applied artificial intelligence has led to various applications, but current software implementation is limited in terms of performance and energy efficiency. Further progress may require the development of neuromorphic systems mimicking the structure of the human brain. By utilizing an optical network of nodes and semiconductor microcavities, efficient computation with nonlinearity can be achieved, showing promising results in pattern recognition tasks. This work opens up possibilities for ultrafast and energy-efficient neuromorphic systems leveraging the optical nonlinearity of polaritons.