  This paper proposes DISQ to craft a stable landscape for VQA training and
tackle the noise drift challenge. DISQ adopts a "drift detector" with a
reference circuit to identify and skip iterations that are severely affected by
noise drift errors. Specifically, the circuits from the previous training
iteration are re-executed as a reference circuit in the current iteration to
estimate noise drift impacts. The iteration is deemed compromised by noise
drift errors and thus skipped if noise drift flips the direction of the ideal
optimization gradient. To enhance noise drift detection reliability, we further
propose to leverage multiple reference circuits from previous iterations to
provide a well founded judge of current noise drift. Nevertheless, multiple
reference circuits also introduce considerable execution overhead. To mitigate
extra overhead, we propose Pauli-term subsetting (prime and minor subsets) to
execute only observable circuits with large coefficient magnitudes (prime
subset) during drift detection. Only this minor subset is executed when the
current iteration is drift-free. Evaluations across various applications and
QPUs demonstrate that DISQ can mitigate a significant portion of the noise
drift impact on VQAs and achieve 1.51-2.24x fidelity improvement over the
traditional baseline. DISQ's benefit is 1.1-1.9x over the best alternative
approach while boosting average noise detection speed by 2.07x
