Test transfer of the controlled diagnostic findings to trainable benchmark architectures

Investigate whether the complementarity findings obtained with fixed pair circuits and ridge-regression heads transfer to the trainable nine-qubit variational classifiers and attention-fusion architectures used in the benchmark experiments.

Background

The angular–polynomial diagnostic uses fixed pair circuits, explicitly selected readouts, and ridge regression to isolate complementary angular and polynomial target relationships. The benchmark studies instead use trainable nine-qubit circuits and neural attention or concatenation-based fusion.

The paper identifies applying the trainable benchmark models to the same controlled targets, with matched classical alternatives and selection budgets, as the direct next test. This transfer remains unresolved because the current experiments do not establish that the diagnostic conclusions persist under the more complex trainable architectures.