Reinforcement Learning for Entanglement Distribution in Quantum Networks
Distributing entanglement efficiently across quantum networks remains a major challenge, primarily due to the intricate timing and coordination required for entanglement generation and swapping. Orchestrating these operations with precision is key to achieving efficient and reliable entanglement distribution while hardware performance is improved.
Authors
- Vicente Martín Ayuso (ORCID: https://orcid.org/0000-0002-2559-3979)
- Andrés Agustí (ORCID: https://orcid.org/0000-0002-3160-6609)
- Javier Faba (ORCID: https://orcid.org/0000-0002-3309-431X)
- A. Olivas
- L. M. Robledo (ORCID: https://orcid.org/0000-0002-6061-1319)
- Laura Ortiz Martín (ORCID: https://orcid.org/0000-0002-3768-3998)
Institutions
- Universidad Autónoma de Madrid (ES)
- Universidad Politécnica de Madrid (ES)
Publication Details
- Published
- 2025-08-19
- DOI
- https://doi.org/10.1145/3749096.3750028
- Primary Topic
- Quantum Information and Cryptography
- Type
- article
- Field-Weighted Citation Impact
- 0.00