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

Key points

• Variational quantum algorithms are often limited by barren plateaus: gradients vanish as circuit size and depth increase, making quantum neural networks difficult to train.1
• By restricting the dominant support of each observable to a small capsule and controlling inter-capsule entanglement, Q-Capsule preserves useful gradient signals while retaining communication between local quantum representations.1
• As the register width increases, Q-Capsule consistently maintains stable gradient variance, whereas globally entangling baselines exhibit exponential suppression with a log-gradient-variance slope near -ln 2 per qubit.1
• Q-Capsule also produces more structured optimization landscapes, higher parameter efficiency, improved robustness to depolarizing noise, and lower measurement requirements.1

Sources (1)

• [1]Q-Capsule: A Localized Capsule-Based Quantum Neural Architecture for Barren Plateau Mitigation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:14 AM

  We propose Q-Capsule, a localized capsule-based quantum neural architecture that mitigates this problem through register partitioning, local readout, sparse inter-capsule coupling, trainable data re-uploading, and Quantum Fisher Information Matrix (QFIM)-guided adaptive depth growth.

  Variational quantum algorithms are often limited by barren plateaus: gradients vanish as circuit size and depth increase, making quantum neural networks difficult to train.

Extractive summary: sentences quoted from the sources.