In single-port non-Hermitian sensors the Petermann factor offsets susceptibility gains, imposing a strict resource bound on metrological precision. We test whether a multi-port geometry can evade this bound: a double-chain optomechanical ladder with o
In single-port non-Hermitian sensors the Petermann factor offsets susceptibility gains, imposing a strict resource bound on metrological precision. We test whether a multi-port geometry can evade this bound: a double-chain optomechanical ladder with opposing non-reciprocal hoppings spatially separates signal amplification from quantum-noise drainage, and gradient-based differentiable optimal control (DOC) maximizes the resource-normalized Fisher information \Fnorm subject to a Hurwitz-stability constraint. Across system sizes N\in\{\num{6},\dots,\num{16}\} the optimizer returns \Fnorm>0 in every case, with two coexisting solution classes whose selection is initialization-dependent: deep-stability configurations achieve \Fnorm\in\numrange{0.937}{0.987} with attenuated transmission, while marginal-stability configurations deliver directional gain \Gfwd\in\qtyrange{13.5}{15.5}{\dB} with isolation \Iso\in\qtyrange{40}{64}{\dB}. A multi-restart ensemble reveals these classes are the endpoints of a precision–gain frontier. All solutions remain Hurwitz-stable under \qty{5}{\percent} disorder (\qty{87.5}{\percent} recovery), and the deep-stability advantage survives realistic preamplifier noise at \Fnormeff\approx\num{0.3}–\num{0.5}. Mapped onto circuit-QED parameters, the architecture enables sub-attonewton force sensing and broadband axion searches across the \qtyrange{1}{10}{\giga\hertz} band.