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Genqo is an open-source tool for modeling quantum entanglement sources such as SPDC and ZALM. Genqo uses a hybrid Gaussian/non-Gaussian framework that avoids truncating the quantum state with a low mean photon number approximation. This allows the hybrid approach, unlike comparable perturbative approaches, to predict performance metrics accurately beyond the
Comparison of the hybrid ZALM model to analytical models using the low mean photon number approximation. Divergence of hybrid model from truncated models is evident after Ns = 0.2.
Genqo allows for calculation of performance metrics such as entanglement rate, fidelity, and full density operators while sweeping mean photon number, dark counts, and various device and transmission losses. Thanks to Julia's JIT compilation feature, these metrics can be computed up to three orders of magnitude (130-1000x) faster than in Genqo's original Python implementation.
Comparison of Genqo benchmarks before and after moving to Julia implementation.
Genqo.jl allows for simple integration with the full-stack quantum networking testbed QuantumSavory.jl, or usage as a standalone Python package through the Python wrapper.
Simply install Genqo.jl using Pkg:
using Pkg
Pkg.add("Genqo")Try running the tutorial notebook for an introduction to Genqo.jl's structure and functionality.
Please reach out at jacob.r.gunnell@gmail.com with any questions or ideas.