WTHaar-Net: a Hybrid Quantum-Classical Approach
Convolutional neural networks rely on linear filtering operations that can be reformulated efficiently in suitable transform domains. At the same time, advances in quantum computing have shown that certain structured linear transforms can be implemented with shallow quantum circuits, opening the door to hybrid quantum-classical approaches for enhancing deep learning models. In this work, we introduce WTHaar-Net, a convolutional neural network that replaces the Hadamard Transform used in prior hybrid architectures with the Haar Wavelet Transform (HWT). Unlike the Hadamard Transform, the Haar transform provides spatially localized, multi-resolution representations that align more closely wi...
| Authors | Vittorio Palladino, Tsai Idden, Ahmet Enis Cetin |
|---|---|
| Primary category | cs.CV |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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