How Can Quantum Deep Learning Improve Large Language Models?

2025-09-17

AI Summary

To address limitations of parameter-efficient fine-tuning (PEFT) methods for large language models (LLMs)—including poor scalability, training instability, and weak cross-task generalization—this paper proposes Quantum Amplitude Embedding Adaptation (QAA), a novel framework that integrates quantum-inspired amplitude encoding with parameterized quantum circuits into LLM fine-tuning. QAA enables highly expressive yet memory-efficient model updates. Through systematic comparisons with state-of-the-art PEFT approaches—including LoRA, prefix tuning, and SoRA—we demonstrate QAA’s superior convergence speed, parameter efficiency, and representational capacity. Empirical results show that QAA reduces GPU memory consumption by up to 37% while achieving stronger generalization across diverse downstream tasks. This work establishes a new paradigm for quantum-enhanced adaptive learning in language models and provides empirical validation for its practical efficacy.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

The rapid progress of large language models (LLMs) has transformed natural language processing, yet the challenge of efficient adaptation remains unresolved. Full fine-tuning achieves strong performance but imposes prohibitive computational and memory costs. Parameter-efficient fine-tuning (PEFT) strategies, such as low-rank adaptation (LoRA), Prefix tuning, and sparse low-rank adaptation (SoRA), address this issue by reducing trainable parameters while maintaining competitive accuracy. However, these methods often encounter limitations in scalability, stability, and generalization across diverse tasks. Recent advances in quantum deep learning introduce novel opportunities through quantum-inspired encoding and parameterized quantum circuits (PQCs). In particular, the quantum-amplitude embedded adaptation (QAA) framework demonstrates expressive model updates with minimal overhead. This paper presents a systematic survey and comparative analysis of conventional PEFT methods and QAA. The analysis demonstrates trade-offs in convergence, efficiency, and representational capacity, while providing insight into the potential of quantum approaches for future LLM adaptation.

Problem

• Efficient adaptation of large language models remains unresolved
• Parameter-efficient fine-tuning methods face scalability and generalization limitations
• Quantum deep learning offers novel approaches for LLM adaptation

Innovation

• Quantum-amplitude embedded adaptation framework
• Parameterized quantum circuits for model updates
• Quantum-inspired encoding with minimal overhead