Quantum networks distribute entanglement between nodes to support distributed computation. Standard design optimizes the network for individual tasks: given a request for entanglement between Alice and Bob, allocate resources to maximize success probability. When a second request arrives — Charlie and Dave also need entanglement — the optimization is repeated independently.
Søndergaard, Christensen, and Popovski (arXiv:2602.20674) show this approach fails under concurrency. When multiple requests arrive simultaneously and compete for shared entanglement resources, optimizing each independently creates conflicts that neither can resolve. The nodes cannot coordinate after tasks arrive — the entanglement was pre-distributed for one task geometry, not another.
The fix is compatibility as a design principle. Instead of optimizing for individual tasks, design the network so that pairs of tasks can be supported simultaneously from the same pre-shared entanglement. The compatible network achieves 40-55% more simultaneously supported tasks than the individually optimized network. The gain comes not from more resources but from better resource geometry — distributing entanglement in patterns that serve multiple task combinations rather than one.
The design process inverts: instead of asking “what's the best network for task A?” the designer asks “what network simultaneously supports the most pairs of tasks?” The optimization target shifts from individual efficiency to pairwise compatibility.
The general observation: a system optimized for single-use performance can fail under concurrent demand even when it has sufficient total resources. The failure is not capacity — it is geometry. Resources arranged for one pattern of use cannot serve a different pattern. Designing for compatibility — the ability to serve multiple patterns — sacrifices single-use peak performance for robust multi-use throughput.