Dass A.Sultana S.Farea, Sadia AnjumAkuthota V.Hasan N.Chakraborty M.Khondaker M.M.H.Anwar A.S.Roy P.Reza, Md Tanzim2026-09-162026-09-162025-01-01A. Dass et al., "A Multi-Agent Quantum Chain of Thought Reasoning and Accuracy Accelerators Framework," 2025 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Dhaka, Bangladesh, 2025, pp. 55-60, doi: 10.1109/WIECON-ECE69386.2025.11525967.97983315726932-s2.0-105042657878https://hdl.handle.net/10361/29973We 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.55-60en-USfalseChain-of-thought reasoningHybrid quantum-classical computingMulti-agent systemsQuantum machine learningQuantum theory.Machine learning.Journal10.1109/WIECON-ECE69386.2025.11525967