Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
Sensors (Basel). 2023 May 18;23(10):4852. doi: 10.3390/s23104852.
Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big data. Smart factory systems rely primarily on the analysis results to optimize production, predict future market directions, prevent and manage risks, and so on. However, deploying the existing classical solutions such as machine learning, cloud, and AI is not effective anymore. Smart factory systems and industries need novel solutions to sustain their development. On the other hand, with the fast development of quantum information systems (QISs), multiple sectors are studying the opportunities and challenges of implementing quantum-based solutions for a more efficient and exponentially faster processing time. To this end, in this paper, we discuss the implementation of quantum solutions for reliable and sustainable IIoT-based smart factory development. We depict various applications where quantum algorithms could improve the scalability and productivity of IIoT systems. Moreover, we design a universal system model where smart factories would not need to acquire quantum computers to run quantum algorithms based on their needs; instead, they can use quantum cloud servers and quantum terminals implemented at the edge layer to help them run the desired quantum algorithms without the need of an expert. To prove the feasibility of our model, we implement two real-world case studies and evaluate their performance. The analysis shows the benefits of quantum solutions in different sectors of smart factories.
如今，工业物联网 (IIoT) 和智能工厂正依靠智能和大数据分析来进行大规模决策。然而，由于大数据的复杂性和异构性，这种方法在计算和数据处理方面面临着严峻的挑战。智能工厂系统主要依赖于分析结果来优化生产、预测未来市场方向、预防和管理风险等。然而，部署现有的经典解决方案，如机器学习、云计算和人工智能，已经不再有效。智能工厂系统和行业需要新的解决方案来维持其发展。另一方面，随着量子信息系统 (QIS) 的快速发展，多个领域都在研究实施基于量子的解决方案的机会和挑战，以实现更高效和指数级更快的处理时间。为此，在本文中，我们讨论了为可靠和可持续的基于 IIoT 的智能工厂发展实施量子解决方案。我们描述了各种应用场景，量子算法可以提高 IIoT 系统的可扩展性和生产力。此外，我们设计了一个通用系统模型，智能工厂无需为运行量子算法而购买量子计算机，而是可以根据需要使用量子云服务器和边缘层实现的量子终端来帮助他们运行所需的量子算法，而无需专家的帮助。为了证明我们模型的可行性，我们实现了两个真实案例研究并评估了它们的性能。分析表明了量子解决方案在智能工厂不同领域的优势。
