Machine Learning–Enhanced Quantum-Classical Coexistence Framework at OFC 2024

We are thrilled to highlight a new contribution from the Allegro EU Project, presented at OFC 2024:

R. Yang, R. Wang, A. Seferidis, T. Omigbodun, S. Bahrani, R. D. Oliveira, R. Nejabati, and D. Simeonidou,
“A Machine Learning-Assisted Quantum and Classical Co-existence System,” OFC 2024, session M2J.2.
🔗 Read the abstract here: https://opg.optica.org/abstract.cfm?uri=OFC-2024-M2J.2

Key Highlights:

  • Proposes a machine learning–driven approach to manage the coexistence of quantum and classical signals within the same C-band fiber.
  • Optimizes critical parameters such as channel allocations, power levels, and fiber span, determining the viable coexistence regime.
  • Demonstrates adaptability across varying fiber lengths and network configurations.

This innovation marks an important step toward practical integration of quantum-secure communication within classical optical infrastructure.