QNPack: Modeling and Simulation of Quantum Repeater Networks

QNPack is a simulation toolkit for modeling and analyzing both 1G (memory-based) and All-Photonic quantum repeater architectures.
It enables rapid prototyping and experimentation using NetSquid-based simulations, supporting advanced concepts such as entanglement swapping, purification, and error correction.

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Quantum Repeater Architectures Modeled

1. All-Photonic Entanglement-Based Quantum Repeater (APE-QR)

• Performs end-to-end entanglement and quantum error correction using photonic graph states
• Efficient and deterministic graph state generation using solid-state quantum emitters
• Utilizes Time-Division Multiple Access (TDMA) for state generation, transmission, and reception
• Implements loss-tolerant, measurement-based quantum error correction

2. 1G Trapped-Ion Quantum Repeater (1G-Trapped-Ion-QR)

• Traditional memory-based repeater model with ion-trap qubits
• High-rate, high-fidelity ion-photon entanglement generation
• Supports core repeater functions:
  • Entanglement Generation
  • Entanglement Swapping
  • Entanglement Purification
• Photon-mediated entanglement links between nodes

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Example Experiments

1G Examples (Trapped-Ion Quantum Repeaters)

Provided as Jupyter Notebooks:

• First 1G Simulation: Introductory simulation.
• Parameters for 1G: Guide to selecting network parameters.
• Scaling 1G Simulation: Demonstrates scaling to more repeater nodes.

Terminal Execution:

python qnpack/oneG/iontrap.py

Parameters can be modified via the parameters.yml file.

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APE Examples (All-Photonic Repeaters)

Provided as Jupyter Notebooks:

• First APE Simulation: Introductory simulation.
• Parameters for APE: Guide to choosing simulation parameters.
• Scaling APE Simulation: Demonstrates scaling the network with additional repeater nodes.

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Getting Started

Environment Setup

JupyterHub (JH)

QNPack is pre-installed inside the JupyterHub container image.
If you're using JupyterHub, you can run the example notebooks without any setup.

Source Installation

1. Install NetSquid and Dependencies
   Register at http://netsquid.org and install the following packages:

   pip3 install --extra-index-url https://pypi.netsquid.org netsquid netsquid-trappedions netsquid-netconf

2. Install QNPack
   From inside the QNPack source directory:

   pip3 install -r requirements.txt
   pip install -e .

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