Layer-wise QUBO-Based Training of CNN Classifiers for Quantum Annealing
Variational quantum circuits for image classification suffer from barren plateaus, while quantum kernel methods scale quadratically with dataset size. We propose an iterative framework based on Quadratic Unconstrained Binary Optimization (QUBO) for training the classifier head of convolutional neural networks (CNNs) via quantum annealing, entirely avoiding gradient-based circuit optimization. Following the Extreme Learning Machine paradigm, convolutional filters are randomly initialized and frozen, and only the fully connected layer is optimized. At each iteration, a convex quadratic surrogate derived from the feature Gram matrix replaces the non-quadratic cross-entropy loss, yielding an ...
| Authors | Mostafa Atallah, Rebekah Herrman |
|---|---|
| Primary category | quant-ph |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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