This research introduces Quantum Weighted Tensor Hybrid Networks (QWTHN) to overcome the expressive limitations of traditional Low-Rank Adaptation (LoRA) methods in fine-tuning large language models.

• Leverages quantum computing principles to enhance model adaptability for complex tasks
• Addresses the fundamental constraints of classical low-rank representations
• Offers improved performance while maintaining parameter efficiency
• Demonstrates potential applications in specialized domains requiring nuanced adaptation

The quantum-enhanced approach represents a significant advancement for engineering more flexible, efficient fine-tuning methods that can better handle high-rank dependencies in specialized LLM applications.

Quantum-Enhanced LLM Efficient Fine Tuning