Perspectives of quantum computing for chemical engineering. Issue 6 (7th March 2022)

• Record Type: Journal Article
• Authors: Bernal, David E.; Ajagekar, Akshay; Harwood, Stuart M.; Stober, Spencer T.; Trenev, Dimitar; You, Fengqi
• Is Part Of: AIChE journal. Volume 68:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-07
• Subjects: chemical engineering -- computational chemistry -- machine learning -- optimization -- quantum computing and algorithms; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.17651
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract: Quantum computing has been attracting public attention recently. This interest is driven by the advancements in hardware, software, and algorithms required for its successful usage and the promise that it entails the potential acceleration of computational tasks compared to classical computing. This perspective article presents a short review on quantum computing, how this computational approach solves problems, and three fields that quantum computing can potentially impact the most while relevant to chemical engineering: computational chemistry, optimization, and machine learning. Here, we present a series of chemical engineering applications, the developments, potential improvements with respect to classical computing, and challenges that quantum computing faces for each of these fields. This article intends to provide a clear picture of the challenges and potential advantages that quantum technology may yield for chemical engineering, together with an invitation for our colleagues to join us in the adoption and development of quantum computing.