A Self-Similar Sine-Cosine Fractal Architecture for Multiport Interferometers

TL;DR

This paper introduces a self-similar, modular multiport interferometer architecture based on Sine-Cosine fractal decomposition, enhancing scalability, robustness, and efficiency for photonic applications like quantum computing and neural networks.

Contribution

It proposes a novel self-similar fractal design for multiport interferometers that improves modularity, robustness, and scalability over traditional architectures.

Findings

Robust performance under large fabrication errors

Reduced hardware footprint through systematic truncation

Enhanced resilience to hardware imperfections

Abstract

Multiport interferometers based on integrated beamsplitter meshes have recently captured interest as a platform for many emerging technologies. In this paper, we present a novel architecture for multiport interferometers based on the Sine-Cosine fractal decomposition of a unitary matrix. Our architecture is unique in that it is self-similar, enabling the construction of modular multi-chiplet devices. Due to this modularity, our design enjoys improved resilience to hardware imperfections as compared to conventional multiport interferometers. Additionally, the structure of our circuit enables systematic truncation, which is key in reducing the hardware footprint of the chip as well as compute time in training optical neural networks, while maintaining full connectivity. Numerical simulations show that truncation of these meshes gives robust performance even under large fabrication errors. This design is a step forward in the construction of large-scale programmable photonics, removing a major hurdle in scaling up to practical machine learning and quantum computing applications.