| _version_ | 1866901692545499136 |
|---|---|
| author | Rath, Anshul Chaphekar, Atharva Kohli, Raghav |
| author_facet | Rath, Anshul Chaphekar, Atharva Kohli, Raghav |
| contents | <p dir="auto">This repository implements a memory-based quantum repeater as a Gymnasium environment formulated as a Markov decision process. A deep reinforcement learning agent based on the Proximal Policy Optimization (PPO) algorithm is trained to dynamically control memory discard and entanglement swapping decisions. The learned policies are evaluated based on their ability to optimize the secret key rate in quantum key distribution tasks and are compared against static memory cutoff baseline strategies.</p> <p dir="auto">This work is inspired by the reinforcement learning agents developed in the repository: <a href="https://github.com/SimonReiss/Master-Thesis">https://github.com/SimonReiss/Master-Thesis</a> and the method outlined in the paper: <a href="https://doi.org/10.1103/PhysRevA.108.012406" rel="nofollow">https://doi.org/10.1103/PhysRevA.108.012406</a></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18249278 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Deep Reinforcement Learning for Optimizing Quantum-Memory Based Quantum-Repeaters Rath, Anshul Chaphekar, Atharva Kohli, Raghav Quantum Computing <p dir="auto">This repository implements a memory-based quantum repeater as a Gymnasium environment formulated as a Markov decision process. A deep reinforcement learning agent based on the Proximal Policy Optimization (PPO) algorithm is trained to dynamically control memory discard and entanglement swapping decisions. The learned policies are evaluated based on their ability to optimize the secret key rate in quantum key distribution tasks and are compared against static memory cutoff baseline strategies.</p> <p dir="auto">This work is inspired by the reinforcement learning agents developed in the repository: <a href="https://github.com/SimonReiss/Master-Thesis">https://github.com/SimonReiss/Master-Thesis</a> and the method outlined in the paper: <a href="https://doi.org/10.1103/PhysRevA.108.012406" rel="nofollow">https://doi.org/10.1103/PhysRevA.108.012406</a></p> |
| title | Deep Reinforcement Learning for Optimizing Quantum-Memory Based Quantum-Repeaters |
| topic | Quantum Computing |
| url | https://doi.org/10.5281/zenodo.18249278 |