A machine learning model was developed to predict the discrepancy between ideal and experimentally measured quantum circuit output distributions when executing circuits on a real quantum hardware backend. The target quantity is the total variation distance (TVD) between the ideal distribution obtained from classical simulation and the distribution observed from hardware execution. The goal is to determine whether structural properties of a circuit, together with information derived from ideal simulation, can be used to estimate how strongly hardware noise will distort the circuit’s output.
The dataset consists of families of parameterized quantum circuits with varying depth, entangling structure, and topology. For each circuit, the ideal output distribution is computed using a classical simulator, while the same circuit is executed on a real quantum processing backend to obtain the measured output distribution. From these circuits, features describing the circuit structure (such as depth and two-qubit gate structure) and statistics of the ideal output distribution are extracted and used as inputs to a regression model.
A ML algorithm trained on these features achieves strong predictive performance when evaluated on a held-out test set drawn from the same circuit family distribution, achieving an R2 score of approximately 0.57 with a mean absolute error of about 0.033 TVD units. The model also shows a strong rank correlation (Spearman ρ≈0.82), indicating that it can reliably order circuits from more to less noise-sensitive even when exact prediction errors remain.
To test generalization beyond the training distribution, an additional experiment was conducted in which the model was trained on circuits belonging to two circuit topology families and evaluated on circuits from a third, previously unseen topology. Under this topology shift, predictive performance decreases to R2≈0.19 with a mean absolute error of approximately 0.048 and a rank correlation of ρ≈0.66. Although absolute prediction accuracy drops under this distribution shift, the model still preserves moderate ability to rank circuits by expected deviation from ideal behavior.
Overall, these results suggest that circuit-level structural features combined with ideal-simulation statistics contain meaningful information about hardware noise sensitivity. While accurate regression across unseen circuit families remains challenging, the model demonstrates promising capability for estimating relative circuit robustness prior to execution on quantum hardware that is extremely useful for high intensive and expensive workloads.