Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers
The study investigated whether combining Q-FLAIR's adaptive circuit growth with Caro et al.'s data efficiency bound leads to a predictable scaling law between training data size and circuit complexity. The results showed no predictable relationship: circuit size and accuracy vary non-monotonically with data, suggesting that the combination of these two methods does not automatically guarantee an optimal joint scaling strategy.