Fig. 1: Illustrative example of a NetSquid use case.

Each sub-figure explains part of the modelling and simulation process. For greater clarity the figures are not based on real simulation data. The scenario shown is a quantum repeater utilising entanglement distillation (see main text). a The setup of a quantum network using node and connection components. b A zoom in showing the subcomponents of the entangling connection component. The quantum channels are characterised using fibre delay and loss models. The quantum source samples from an entangled bipartite state sampler when externally triggered by the classical channel. c A zoom in of the quantum memory positions within a quantum processor illustrating their physical gate topology. The physical single-qubit instructions possible on each memory in this example are the Pauli (X, Y, Z), Hadamard (H), and X-rotation (R_X) gates, and measurement. The blue-dashed arrows show the positions and control direction (where applicable) for which the two-qubit instructions controlled-X (CNOT) and swap are possible. Noise and error models for the memories and gates are also assigned. d Illustration of a single simulation run. Time progresses by discretely stepping from event to event, with new events generated as the simulation proceeds. Qubits are represented by circles, which are numbered according to the order they were generated. A star shows the moment of generation. The curved lines between qubits denote their entanglement with the colour indicating fidelity. The state of each qubit is updated as it is accessed during the simulation, for instance to apply time-dependent noise from waiting in memory. e A zoom in of the distillation protocol. The shared quantum states of the qubits are combined in an entangling step, which then shrinks as two of the qubits are measured. The output is randomly sampled, causing the simulation to choose one of two paths by announcing success or failure. f A plot illustrating the stochastic paths followed by multiple independent simulation runs over time, labelled by their final end-to-end fidelity F_i. The blue dashed line corresponds to the run shown in (d). The runs are typically executed in parallel. Their results are statistically analysed to produce performance metrics such as the average outcome fidelity and run duration.