A multi-agent quantum chain of thought reasoning and accuracy accelerators framework

Abstract

We present the Quantum Chain of Thought (QCoT) Reasoning and Accuracy Accelerators Framework, a novel multiagent quantum-classical hybrid architecture that significantly outperforms existing machine learning and LLM-based approaches in complex reasoning tasks. While recent studies show that fine-tuning LLMs like Llama, Gemma, and Mistral achieve modest improvements (10-32% accuracy gains) in sentiment analysis tasks, and deep learning approaches for image classification reach up to 68% accuracy with optimization techniques, our quantum multi-agent framework demonstrates superior performance with 20.90% accuracy improvement over baseline quantum models and 45 - 60% improvement over traditional ML approaches. Our framework integrates three specialized quantum agentsa Structured Chain-of-Thought Quantum Agent implementing 5-step cognitive reasoning, a Semantic Analyzer Agent with multi-pass inference capabilities, and a Quantum Error Correction Agent utilizing surface code simulation. Unlike conventional single-agent quantum approaches that struggle with complex reasoning, our multi-agent orchestration enables quantum systems to handle intricate cognitive processes more effectively.