Workload–Mereology Quantum Computing: When Computational Demand Changes the Preferred Quantum Subsystem Structure

Quantum subsystem structure is typically specified independently of the workload, yet the decomposition that best preserves locality under a Hamiltonian need not be the one that best supports repeated computation. We develop workload–mereology quantum computing, a framework in which generalized tensor-product structure is selected by an explicit competition between dynamical naturalness and computational demand. Physical cost is quantified using the Gaussian scrambling criterion of operational quantum mereology, while task cost measures workload interactions cut by a candidate bipartition. After dimensionless normalization, their competition is captured by Jλ(F) = Ŝ(F) + λĈₜ(F). We prove that a factorization is uniquely optimal for some workload pressure precisely when its cost pair is an exposed point of the lower convex hull, and that a genuine compromise remains optimal over a nonempty interval precisely when it forms a strict intermediate lower-hull vertex. A four-qubit construction yields the transition threshold in closed form. Exhaustive reproducible simulations show that compromise factorizations are not exceptional: at n = 10 and λ = 1, they occur in 61.33% of 300 commuting Pauli-ZZ instances for uniform and lognormal couplings (60.67% for normal couplings) and in 59.00% of generic noncommuting XX+YY+ZZ instances. These results identify a task-dependent regime of quantum subsystem organization in which workload does more than act on a predetermined decomposition: it becomes a variational ingredient in determining which decomposition is preferred.