Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers

Saeefa Rubaiyet Nowmi, Md Mahmuduzzaman Kamol, Mohammad Saidur Rahman|October 8, 2026arXiv

Key Takeaway

Quantum circuit growth algorithms don't follow predictable scaling laws with training data size, and theoretical generalization bounds, while valid, have weak predictive power for practical circuit design decisions.

Summary

This paper investigates whether quantum circuit complexity and training data requirements follow predictable scaling laws. Researchers reimplemented Q-FLAIR, an algorithm that grows quantum circuits gate-by-gate, and tested it on MNIST classification with varying dataset sizes.

training scaling evaluation

Key Terms

quantum-circuit-synthesis generalization-bound active-parameter-count scaling-law