Abstract

Matrix-vector multiplications (MVMs) are essential for a wide range of applications,
particularly in modern machine learning and quantum computing. In photonics, there is growing
interest in developing architectures capable of performing linear operations with high speed, low
latency, and minimal loss. Traditional interferometric photonic architectures, such as the Clements
design, have been extensively used for MVM operations. However, as these architectures scale,
improving stability and robustness becomes critical. In this paper, we introduce a novel photonic
braid interferometer architecture that outperforms both the Clements and Fldzhyan designs in
these aspects. Using numerical simulations, we evaluate the performance of these architectures
under ideal conditions and systematically introduce non-idealities such as insertion losses, beam
splitter imbalances, and crosstalk. The results demonstrate that the braid architecture offers
superior robustness due to its symmetrical design and reduced layer count. Further analysis
shows that the braid architecture is particularly advantageous in large-scale implementations,
delivering better performance as the size of the interferometer increases. We also assess the
footprint and total insertion losses of each architecture. Although waveguide crossings in the
braid architecture slightly increase the footprint and insertion loss, recent advances in crossing
technology significantly minimize these effects. Our study suggests that the braid architecture is
a robust solution for photonic neuromorphic computing, maintaining high fidelity in realistic
conditions where imperfections are inevitable.

Related Research Topics

• Reconfigurable Photonic Circuits

Related Projects

• Horizon Europe: Neuropuls