Regression of Functions by Quantum Neural Networks Circuits

TL;DR

This paper introduces a genetic-algorithm-based method for automatically designing quantum neural network architectures for regression, demonstrating competitive performance with fewer parameters and using dataset complexity metrics for effective model selection.

Contribution

It presents a novel automated quantum architecture search framework for regression tasks and shows how dataset complexity metrics can predict optimal quantum models.

Findings

Quantum models are compact yet competitive with classical methods.

Dataset complexity metrics reliably predict the best quantum architecture.

Meta-learning scenarios achieve high accuracy in model selection.

Abstract

The performance of quantum neural network models depends strongly on architectural decisions, including circuit depth, placement of parametrized operations, and data-encoding strategies. Selecting an effective architecture is challenging and closely related to the classical difficulty of choosing suitable neural-network topologies, which is computationally hard. This work investigates automated quantum-circuit construction for regression tasks and introduces a genetic-algorithm framework that discovers Reduced Regressor QNN architectures. The approach explores depth, parametrized gate configurations, and flexible data re-uploading patterns, formulating the construction of quantum regressors as an optimization process. The discovered circuits are evaluated against seventeen classical regression models on twenty-two nonlinear benchmark functions and four analytical functions. Although classical methods often achieve comparable results, they typically require far more parameters, whereas the evolved quantum models remain compact while providing competitive performance. We further analyze dataset complexity using twelve structural descriptors and show, across five increasingly challenging meta-learning scenarios, that these measures can reliably predict which quantum architecture will perform best. The results demonstrate perfect or near-perfect predictive accuracy in several scenarios, indicating that complexity metrics offer powerful and compact representations of dataset structure and can effectively guide automated model selection. Overall, this study provides a principled basis for meta-learning-driven quantum architecture design and advances the understanding of how quantum models behave in regression settings--a topic that has received limited exploration in prior work. These findings pave the way for more systematic and theoretically grounded approaches to quantum regression.