A Modular Software Stack for Quantum Computing From Born-Rule Equilibrium to Nonequilibrium-Aware Quantum Software

Current quantum software architectures rely on the Born rule as a universal axiom for converting quantum states into measurement statistics. In practice, near-term (NISQ) processors operate as noisy, driven open systems, where equilibrium assumptions are not guaranteed and systematic deviations can bias algorithmic outcomes. We introduce a Modular Software Stack for Quantum Systems in which the Born rule is reinterpreted as an operational equilibrium fixed point associated with modular (KMS) balance. A modular diagnostics layer, based on the Modular Imbalance Score (MIS), identifies reproducible nonequilibrium structure directly from experimentally reconstructed reduced states and applies a universal, normalization-preserving correction when warranted. We validate the framework on superconducting quantum hardware, 1 demonstrating statistically significant modular imbalance and automated model selection via information criteria. As an algorithmic application, we show that modular-aware post-processing improves the accuracy of a variational quantum eigensolver (VQE) on IBM Quantum devices, outperforming standard Born-based estimation and zero-noise extrapolation in nonequilibrium measurement regimes. Together, these results establish modular diagnostics as a physically grounded, falsifi-able, and scalable software layer that bridges nonequilibrium thermodynamics and practical quantum computing.