Data and code supporting the publication: Tools for Reducing Service Time in Near-Term Quantum Networks

This dataset accompanies the paper "Tools for Reducing Service Time in Near-Term Quantum Networks'' (Smith et al., QCE 2026) and supports the evaluation of analytical tools that reduce the scheduled separation between consecutive batches of entanglement generation attempts in near-term quantum networks. The objective is to quantify the network service time reduction and session execution speedup achievable by replacing a conservative minimum-separation schedule with an optimized scheduled separation, while maintaining the service probability committed to by the network. The work is computational, validating a Markov chain model of session execution with Monte Carlo simulation of packet generation under Poisson-distributed entangled link arrivals within a sliding window. Data was collected by running four Python scripts that sweep the relevant parameters, namely the entanglement generation rate, sliding-window length, requested link count, packet generation probability, and PGA ingress, across state-of-the-art and optimistic trapped-ion hardware regimes. The resulting data comprises tabulated minimum separation violation probabilities used as lookup tables, per-trial and per-cell simulation records, aggregated single-session and multi-session performance summaries, and the corresponding figures and LaTeX tables that appear in the paper.