QPINN framework with QNN-based trainable embeddings solves the lid-driven cavity problem with stable training, competitive accuracy, and fewer parameters than classical PINNs.
A trainable-embedding quantum physics-informed frame- work for multi-species reaction-diffusion systems,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A QPINN Framework with Quantum Trainable Embeddings for the Lid-Driven Cavity Problem
QPINN framework with QNN-based trainable embeddings solves the lid-driven cavity problem with stable training, competitive accuracy, and fewer parameters than classical PINNs.