qnetsur presents a tool to optimize parameters of expensive quantum-network simulations. It is based on simple regression models in sklearn (DecisionTreeRegressor and SVR).
Context: In order to bring quantum networks closer to reality, we need to thoroughly understand quantum network hardware and associated protocols. As physical architectures quickly become too complex for analytical study, the research field widely relies on comprehensive numerical simulations to investigate quantum-network behavior. These simulations can be highly informative, but their functional form is typically unknown. When it comes to optimization, techniques relying on assumptions about the function’s continuity, differentiability, or convexity are thus inapplicable. Additionally, quantum network simulations are computationally demanding, rendering global approaches like simulated annealing or genetic algorithms – which require extensive function evaluations – impractical.
- Python 3.9+
- scipy
- scikit-learn==1.3.1
- numpy
- pandas
Find the official documentation at https://qnetsur.readthedocs.io/
This tutorial (google colab account required) introduces basic usage of qnetsur. It features the optimization of parameters of a quantum network protocol that is based on continuous-entanglement distribution. Check it out!
pip install git+https://github.com/Luisenden/qnetsur