Quantum Computing and Deep Learning for Process Systems Optimization and Data Analytics

The process systems engineering community has been developing computing and systems technologies (CASTs) and applying them to address chemical engineering problems across scales. Quantum computing and deep learning are emerging CASTs that have attracted growing interests from both academia and industry. This presentation will provide a brief introduction to the state-of-the-art of deep learning and quantum computing technologies, and discuss their potential applications in chemical engineering. Specifically, we will present the results of several collaborative research projects to illustrate the multi-scale applications of deep learning on molecular design, materials screening, sensor development, and energy systems control. In the second half of the presentation, we will introduce novel hybrid classical-quantum optimization algorithms that exploit the strengths of quantum computing techniques to address the computational challenges of important product-process systems engineering problems, ranging from molecular design, to manufacturing systems operations, and to supply chain optimization. The presentation will conclude with a novel deep learning model and quantum computing algorithm for efficient and effective fault diagnosis in chemical processes.