Transferred QAOA Parameters Remember the Penalty Scale: A $\lambda$-Resonance Law for Constrained Quantum Optimization
Training the variational angles of the Quantum Approximate Optimization Algorithm once on a small instance and reusing them on larger ones, known as parameter transfer, is the standard route past the exact-simulation wall. Existing literature explains its success almost entirely through structural similarity. We identify a new, independent axis that governs transfer whenever constraints are encoded as penalties: the trained angles memorize the penalty weight $\lambda$ of their training instance. For any scalarized cost function with an integer-valued violation count, we prove that at arbitrary fixed QAOA angles $(\beta,\gamma)$ of depth $p$, the probability mass $F(\lambda)$ on the feasible subspace is a finite real trigonometric polynomial in $\lambda$ whose angular frequencies lie on an integer lattice generated by the trained $\gamma$'s. Three consequences follow immediately: transfer feasibility is a resonance peaked where the deployment penalty matches the training penalty; the resonance width scales as $1/(v_{max}\sum_k|\gamma_k|)$, so low-$|\gamma|$ angle sets are systematically more transferable; and the curve exhibits revival peaks at spacings $2\pi/\gamma_k$. We confirm all three predictions by exact statevector experiments on a 20-qubit multi-user resource-allocation QUBO. The theorem is independent of how the angles were obtained and applies to any integer-penalty QUBO, recasting a widely reported failure mode of penalty-based QAOA as deterministic, predictable phase interference rather than an energetic tuning problem.