Strongly simulating a quantum circuit, that is, computing an output amplitude, can be done by summing the circuit's Feynman paths: a weighted count over assignments to Boolean path variables. The circuit's gates induce correlations among these variables, forming a graph whose structure controls several exact simulation routes. This sum-of-powers (SOP) viewpoint underlies recent simulators built on binary decision diagrams and weighted model counting. For a quadratic SOP with $n$ variables, even modulus $r$, and a rank-decomposition of its variable graph of width $k$, our dynamic program (DP) computes an amplitude using only $O(4^kpoly(n))$ arithmetic operations. For Clifford$+T$ circuits, the amplitude is given by an SOP with modulus $8$. Rank-width never exceeds linear rank-width, which governs some decision-diagram approaches, and is at most one greater than the Markov--Shi contraction complexity of the circuit tensor network. Moreover, there are non-Clifford families of bounded rank-width where both competing parameters diverge. We also present a stabilizer-rank optimization, exploiting that the DP tables are stabilizer-type Gauss sums. Each subtree runs at the width price of its cut-ranks or at a magic price that discharges the non-Clifford phases below it. The resulting best total cost never exceeds $O(4^kpoly(n))$, yet is polynomial on mixed families where the pure rank-width and pure $T$-count guarantees are both exponential. Clifford amplitudes take polynomial time on any graph, the exact-amplitude consequence of Gottesman--Knill. A prototype evaluation on standard circuit benchmarks finds treewidth bucket elimination the strongest baseline, with the new rank-width DP complementary: it wins on structured families where treewidth blows up.

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