Quantum-PEFT: Ultra parameter-efficient fine-tuning

This paper introduces Quantum-PEFT that leverages quantum computations for parameter-efficient fine-tuning (PEFT). Unlike other additive PEFT methods, such as low-rank adaptation (LoRA), Quantum-PEFT exploits an underlying full-rank yet surprisingly parameter efficient _quantum unitary parameterization_. With the use of Pauli parameterization, the number of trainable parameters grows only logarithmically with the ambient dimension, as opposed to linearly as in LoRA-based PEFT methods. Quantum-PEFT achieves vanishingly smaller number of trainable parameters than the lowest-rank LoRA as dimensions grow, enhancing parameter efficiency while maintaining a competitive performance. We apply Quantum-PEFT to several transfer learning benchmarks in language and vision, demonstrating significant advantages in parameter efficiency.

Authors

• Toshiaki Koike-Akino
• Francesco Tonin
• Yongtao Wu
• Frank Zhengqing Wu
• Leyla Naz Candogan
• Volkan Cevher

Keywords

• parameter-efficient fine-tuning
• lora
• quantum machine learning
• orthogonality constraints