Memory Physics, Finite Capacity, and Topology in Heterogeneous Quantum Networks

Quantum-network capacity planning couples memory physics, finite storage, topology, and traf-fic. We study these interactions using a discrete-event simulator with six representative memory profiles. A memory–topology atlas contains 9,360 completed runs, complemented by 8,730 confir-matory runs and an extended per-node memory-budget sweep. We distinguish a threshold relative to the best performance observed on a finite grid from a physical saturation limit. Among 840 re-ported threshold estimates, 22.3% reach 95% of the observed maximum by four slots per node, while 35.5% first reach it at the upper grid boundary of 64 slots. Four tested scalar reductions do not provide a topology-independent collapse; the best pooled quadratic fit explains 37.9% of response variance. The retrieval-window parameter χ_r = τ_r p_gen/t_attempt provides an interpretable descriptor, but an independent-channel approximation does not establish a network memory-sizing law. In 5,310 heterogeneous-placement runs, hub and betweenness-based placement outperform peripheral placement in aggregate, with topology-specific exceptions. Coupling assumptions change absolute performance while preserving the aggregate strategy ordering. Harmonized model comparisons provide limited robustness evidence, not independent native-protocol validation. The results support model-specific capacity and placement analysis with explicit metric definitions, finite-grid limitations, and interface assumptions.