On the importance of fundamental properties in quantum-classical machine learning models
Abstract
Wepresent a systematic study of how quantum circuit design, in particular variational ansatz depth and the choice of quantum feature mapping, affect shybrid quantum classical neural networks for causal classification. The architecture combines a classical convolutional neural network for feature extraction with a parameterized quantum circuit as the quantum layer. We evaluate multiple ansatz depths and nine feature maps. Increasing ansatz repetitions improves generalization and training stability, with benefits saturating at higher depths. Feature mapping choice is more critical since only multi-axis Pauli encodings enable successful learning, while simpler maps lead to underfitting and reduced separability.
Fields of Study & Subjects
Quantum Computing Algorithms and Architecture
Publication details
- Journal / source
- International Journal of Parallel Emergent and Distributed Systems
- Publisher
- Informa UK Limited
- Volume
- 41
- Issue
- 3
- Pages
- 372–399
- Publication date
- 16 February 2026
- Language
- EN