Quantum-NLP.
Under Development
Quantum Native Natural Language Processing experiments utilizing PyTorch, Transformers, PennyLane, and Qiskit for hybrid quantum-classical models.
Core Architecture
Quantum Attention
Experimental attention mechanisms powered by quantum interference patterns to capture long-range semantic dependencies.
Hybrid State
Combines classical word embeddings with quantum state vectors for a richer, multi-dimensional semantic representation.
DisCoCat Models
Implementation of Distributional Compositional Categorical (DisCoCat) frameworks for quantum natural language understanding.
Transformer Integration
Injects quantum subroutines into standard Transformer architectures to explore potential quantum advantages in NLP.