Future Trends

In this chapter, a new overlap of quantum computing and reinforcement learning in heat transfer is discussed, giving a new approach to improvement of the modeling, simulation, and control of thermal systems. Quantum computing can be used to solve optimization and efficiently simulate complex thermal behaviors with high fidelity, and reinforcement learning allows autonomous, adaptive control in a dynamic setting. A combination of the these technologies enables a very attractive paradigm: optimizing system performance and energy efficiency of various applications including those involving smart heating, ventilation, and air-conditioning and smart battery thermal management. Although it explains the nature of the technical development, this work also covers the wider consequences of bringing artificial intelligence into the field of thermal engineering. Among the most important factors to consider are algorithm computational efficiency, sustainability, and ethical aspects like data privacy, algorithm transparency, and shifting dynamics of human expertise. To survey the topic and foresee the findings, the chapter is concluded by discussing current research directions and the disruptive promise that AI- and quantum-enhanced thermal systems hold.