The Internet of Things (IoT) has revolutionized various sectors by enabling seamless interaction between devices. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fall short in addressing these concerns due to the unique characteristics of IoT networks such as heterogeneity, scalability, and resource constraints. To address these challenges, this survey paper first explores the intersection of quantum computing, federated learning, and 6G wireless networks as a novel approach to enhancing IoT security and privacy. In order to enable several secure intelligent IoT applications, quantum computing, with its superior computational capabilities, can strengthen encryption algorithms, making IoT data more secure. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among a vast array of IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, key performance metrics, and summarizing the vital insights, challenges, and trends in the realm of securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks, suggesting potential integration with 7G, and summarizing the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.