Optimization of Quantum-Repeater Networks using Stochastic Automatic Differentiation
Quantum-network simulations are discretely random, making it difficult to extract derivatives. We solve this using a recent machine-learning technique called stochastic automatic differentiation and showcase it by optimizing the placement of quantum repeaters and more.
Authors
- Guus Avis (ORCID: https://orcid.org/0000-0001-7035-8065)
- Stefan Krastanov (ORCID: https://orcid.org/0000-0001-5550-5258)
Institutions
- University of Massachusetts Amherst (US)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/quantum.2025.qw3a.14
- Primary Topic
- Quantum Computing Algorithms and Architecture
- Type
- article
- Field-Weighted Citation Impact
- 0.00