Abstract: Prior to the advent of fault tolerant quantum computers, the ability to execute post quantum cryptographic (PQC) algorithms on noisy intermediate scale quantum (NISQ) devices is crucial to evaluate their security and performance. However, time-varying errors in the gates and measurements not mitigated by static means can compromise cryptographic security, resulting in unacceptably high decapsulation failure rates. This paper presents an adaptive error mitigation framework FF that involves an online noise estimation algorithm, a reinforcement learning agent and a library of mitigation actions such as readout error inversion, probabilistic error cancellation, Clifford assisted transformation, dynamical decoupling, zero noise extrapolation, and qubit remapping. The framework is dynamic and selects optimal actions based on real-time estimates of noise to minimize the total variation distance (TVD) subject to gate overhead and latency constraints. We benchmark FF across three NISQ devices: IBM Brisbane, Rigetti Aspen M 3, and IonQ harmony on the circuits Kyber 512, Dilithium 2 and SPHINCS+ 128f. The adaptive framework improves the TVD by 44-51% when compared to the best static method and 78-83% when compared to no mitigation. Most importantly, only the adaptive framework guarantees Kyber 512's decapsulation failure probability to be below the 2^-128 security threshold on all devices, whereas static methods are found to be above the bound because of the unmodeled noise drift. Gate overhead is kept within 20% and classical latency under 50ms per slice. These results demonstrate that adaptive, learning-driven error mitigation is not merely beneficial, but necessary for provably secure PQC execution on NISQ hardware. The framework and benchmark suite are released open-source to accelerate reproducible research.

Key words: adaptive error mitigation, post-quantum cryptography, NISQ devices, reinforcement learning, cryptographic security margins