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

Quantum-network capacity planning couples memory physics, finite storage, topology, and traffic. We study these interactions with a discrete-event simulator and six representative memory profiles. The simulator is validated against exact single-link, queueing, and swap-ASAP results and against the native SeQUeNCe protocol stack. The native comparison shows that a shared-multiplexing convention used in earlier versions of the model overstates the capacity gain from additional memory, so we report a per-slot model as the primary analysis and the earlier conventions as a sensitivity arm. A single frozen campaign of 266,736 time-based runs with 20 seeds per cell covers an atlas of six profiles, nine topologies, three loads, three network sizes, and seven per-node memory budgets, together with fixed-total-memory, coherence-time, heterogeneous-placement, and Sobol-sensitivity experiments. Under the primary model, the memory budget at which completion reaches 95% of its observed maximum has a median of 8 slots per node, depends more on topology than on memory profile, and reaches the grid boundary of 64 slots in 3.9% of 9688 regimes. Under the earlier conventions, it is strongly profile-dependent, with medians from 4 to at least 64 and 45.8% boundary cases. Four scalar reductions do not collapse the response (pooled quadratic R2 at most 0.152). Topology effects persist at fixed total memory after adjustment for path length. Request completion is independent of the coherence time T2 by construction and is governed by retrieval efficiency, attempt time, and the retrieval decay time τr; T2 enters through fidelity-qualified service. The retrieval-window parameter χr=τrpgen/tattempt orders the profiles by achievable completion but is not a memory-sizing law. Placing high-quality memories at hubs or betweenness bottlenecks outperforms peripheral placement in both model arms and under three interface-coupling rules. Memory-sizing conclusions are therefore sensitive to how multiplexing and storage are modeled and should be validated against native protocol implementations.