Scaling Laws for Hybrid Quantum Neural Networks: Depth, Width, and Quantum-Centric Diagnostics

• URL: http://arxiv.org/abs/2604.06007v1
• Date: Tue, 07 Apr 2026 15:44:30 GMT
• Title: Scaling Laws for Hybrid Quantum Neural Networks: Depth, Width, and Quantum-Centric Diagnostics
• Abstract summary: Hybrid quantum neural networks are increasingly explored for classification.<n>It remains unclear how their performance and quantum behavior scale with circuit depth and qubit count.<n>We present a controlled scaling study of hybrid quantum-classical classifiers along two axes.
• Score: 2.2133667529581933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hybrid quantum neural networks are increasingly explored for classification, yet it remains unclear how their performance and quantum behavior scale with circuit depth and qubit count. We present a controlled scaling study of hybrid quantum-classical classifiers along two axes: (1) increasing the number of quantum layers L at fixed qubits Q, and (2) increasing the number of qubits Q at fixed depth L. Across multiple datasets, we evaluate predictive performance using Accuracy, PR-AUC, Precision, Recall, and F1, and track quantum-specific metrics (QCE, EEE, QGN) to characterize how quantum properties evolve under scaling. Our results summarize scaling trends, saturation regimes, and dataset-dependent sensitivity, and further analyze how quantum metrics relate to predictive performance. This study provides practical guidance for selecting (Q,L) in hybrid QNN classifiers and establishes a consistent evaluation protocol for scaling analysis.

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