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arXiv Machine Learning By Awal Ahmed Fime, Tasfia Zaman Samiha, Saika Zaman, Dimitris Pados, George Sklivanitis, Abdur R. Shahid, Ahmed Imteaj

Q-Capsule: A Localized Capsule-Based Quantum Neural Architecture for Barren Plateau Mitigation

Read the original on arXiv Machine Learning →

Q-Capsule is a localized capsule-based quantum neural architecture designed to mitigate barren plateaus in variational quantum algorithms. It achieves this through register partitioning, local readout, sparse inter-capsule coupling, trainable data re-uploading, and a Quantum Fisher Information Matrix-guided adaptive depth growth. The approach maintains stable gradient variance as register width increases, produces more structured optimization landscapes, improves parameter efficiency and noise robustness, and reduces measurement requirements while achieving high classification accuracy with fewer two-qubit gates.

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